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Loss of biodiversity from lower to upper trophic levels reduces overall productivity and stability of coastal ecosystems in our oceans, but rarely are these changes documented across both time and space. The characterisation of environmental DNA (eDNA) from sediment and seawater using metabarcoding offers a powerful molecular lens to observe marine biota and provides a series of ‘snapshots’ across a broad spectrum of eukaryotic organisms. Using these next-generation tools and downstream analytical innovations including machine learning sequence assignment algorithms and co-occurrence network analyses, we examined how anthropogenic pressures may have impacted marine biodiversity on subtropical coral reefs in Okinawa, Japan. Based on 18 S ribosomal RNA, but not ITS2 sequence data due to inconsistent amplification for this marker, as well as proxies for anthropogenic disturbance, we show that eukaryotic richness at the family level significantly increases with medium and high levels of disturbance. This change in richness coincides with compositional changes, a decrease in connectedness among taxa, an increase in fragmentation of taxon co-occurrence networks, and a shift in indicator taxa. Taken together, these findings demonstrate the ability of eDNA to act as a barometer of disturbance and provide an exemplar of how biotic networks and coral reefs may be impacted by anthropogenic activities.

Environmental DNA (eDNA) surveys are increasingly being used for biodiversity monitoring, principally because they are sensitive and can provide high resolution community composition data. Despite considerable progress in recent years, eDNA studies examining how different environmental sample types can affect species detectability remain rare. Comparisons of environmental samples are especially important for providing best practice guidance on early detection and subsequent mitigation of non-indigenous species. Here we used eDNA metabarcoding of COI (cytochrome c oxidase subunit I) and 18S (nuclear small subunit ribosomal DNA) genes to compare community composition between sediment and water samples in artificial coastal sites across the United Kingdom. We first detected markedly different communities and a consistently greater number of distinct operational taxonomic units in sediment compared to water. We then compared our eDNA datasets with previously published rapid assessment biodiversity surveys and found excellent concordance among the different survey techniques. Finally, our eDNA surveys detected many non-indigenous species, including several newly introduced species, highlighting the utility of eDNA metabarcoding for both early detection and temporal / spatial monitoring of non-indigenous species. We conclude that careful consideration on environmental sample type is needed when conducting eDNA surveys, especially for studies assessing community change.

Environmental DNA (eDNA) analysis allows the simultaneous examination of organisms across multiple trophic levels and domains of life, providing critical information about the complex biotic interactions related to ecosystem change. Here we used multilocus amplicon sequencing of eDNA to survey biodiversity from an eighteen-month (2015–2016) time-series of seawater samples from Monterey Bay, California. The resulting dataset encompasses 663 taxonomic groups (at Family or higher taxonomic rank) ranging from microorganisms to mammals. We inferred changes in the composition of communities, revealing putative interactions among taxa and identifying correlations between these communities and environmental properties over time. Community network analysis provided evidence of expected predator-prey relationships, trophic linkages, and seasonal shifts across all domains of life. We conclude that eDNA-based analyses can provide detailed information about marine ecosystem dynamics and identify sensitive biological indicators that can suggest ecosystem changes and inform conservation strategies. Increasingly, eDNA is being used to infer ecological interactions. Here the authors sample eDNA over 18 months in a marine environment and use co-occurrence network analyses to infer potential interactions among organisms from microbes to mammals, testing how they change over time in response to oceanographic factors.

Anthropogenically forced changes in global freshwater biodiversity demand more efficient monitoring approaches. Consequently, environmental DNA (eDNA) analysis is enabling ecosystem-scale biodiversity assessment, yet the appropriate spatio-temporal resolution of robust biodiversity assessment remains ambiguous. Here, using intensive, spatio-temporal eDNA sampling across space (five rivers in Europe and North America, with an upper range of 20–35 km between samples), time (19 timepoints between 2017 and 2018) and environmental conditions (river flow, pH, conductivity, temperature and rainfall), we characterise the resolution at which information on diversity across the animal kingdom can be gathered from rivers using eDNA. In space, beta diversity was mainly dictated by turnover, on a scale of tens of kilometres, highlighting that diversity measures are not confounded by eDNA from upstream. Fish communities showed nested assemblages along some rivers, coinciding with habitat use. Across time, seasonal life history events, including salmon and eel migration, were detected. Finally, effects of environmental conditions were taxon-specific, reflecting habitat filtering of communities rather than effects on DNA molecules. We conclude that riverine eDNA metabarcoding can measure biodiversity at spatio-temporal scales relevant to species and community ecology, demonstrating its utility in delivering insights into river community ecology during a time of environmental change. Using intensive eDNA sampling in space and time across five rivers in Europe and North America, this study shows that eDNA gives relevant information on freshwater diversity and ecology across broad taxonomic groups, and with limited downstream transport. The findings demonstrate that eDNA is vital for freshwater biodiversity monitoring in a time of anthropogenic change.

Honey bees are large-scale monitoring tools due to their extensive environmental exploration. In their activities and from the hive ecosystem complex, they get in close contact with many organisms whose traces can be transferred into the honey, which can represent an interesting reservoir of environmental DNA (eDNA) signatures and information useful to analyse the honey bee hologenome complexity. In this study, we tested a deep shotgun sequencing approach of honey DNA coupled with a specifically adapted bioinformatic pipeline. This methodology was applied to a few honey samples pointing out DNA sequences from 191 organisms spanning different kingdoms or phyla (viruses, bacteria, plants, fungi, protozoans, arthropods, mammals). Bacteria included the largest number of species. These multi-kingdom signatures listed common hive and honey bee gut microorganisms, honey bee pathogens, parasites and pests, which resembled a complex interplay that might provide a general picture of the honey bee pathosphere. Based on the Apis mellifera filamentous virus genome diversity (the most abundant detected DNA source) we obtained information that could define the origin of the honey at the apiary level. Mining Apis mellifera sequences made it possible to identify the honey bee subspecies both at the mitochondrial and nuclear genome levels.

The alarming declines of freshwater biodiversity call for efficient biomonitoring at fine spatiotemporal scales, such that conservation measures be grounded upon accurate biodiversity data. Here, we show that combining environmental DNA (eDNA) extracted from stream water samples with models based on hydrological first principles allows upscaling biodiversity estimates for aquatic insects at very high spatial resolution. Our model decouples the diverse upstream contributions to the eDNA data, enabling the reconstruction of taxa distribution patterns. Across a 740-km 2 basin, we obtain a space-filling biodiversity prediction at a grain size resolution of 1-km long stream sections. The model’s accuracy in matching direct observations of aquatic insects’ local occurrence ranges between 57–100%. Our results demonstrate how eDNA can be used for high-resolution biodiversity assessments in rivers with minimal prior knowledge of the system. Our approach allows identification of biodiversity hotspots that could be otherwise overlooked, enabling implementation of focused conservation strategies. Biomonitoring via environmental DNA (eDNA) is an important conservation tool for freshwater ecosystems, but this is complicated by eDNA movement downstream. Here, Carraro et al. develop and test an approach to reconstruct high-resolution spatial biodiversity patterns from freshwater eDNA.

Microscopic organisms are often overlooked in traditional diversity assessments due to the difficulty of identifying them based on morphology. Metabarcoding is a method for rapidly identifying organisms where Environmental DNA (eDNA) is used as a template. However, legacy DNA is problematically detected from organisms no longer in the environment during sampling. Environmental RNA (eRNA), which is only produced by living organisms, can also be collected from environmental samples and used for metabarcoding. The aim of this study was to determine differences in community composition and diversity between eRNA and eDNA templates for metabarcoding. Using mesocosms containing field-collected communities from an estuary, RNA and DNA were co-extracted from sediment, libraries were prepared for two loci (18S and COI), and sequenced using an Illumina MiSeq. Results show a higher number of unique sequences detected from eRNA in both markers and higher α-diversity compared to eDNA. Significant differences between eRNA and eDNA for all β-diversity metrics were also detected. This study is the first to demonstrate community differences detected with eRNA compared to eDNA from an estuarine system and illustrates the broad applications of eRNA as a tool for assessing benthic community diversity, particularly for environmental conservation and management applications.

Environmental DNA (eDNA) metabarcoding is revolutionizing the monitoring of aquatic biodiversity. The use of eDNA has the potential to enable non-invasive, cost-effective, time-efficient and high-sensitivity monitoring of fish assemblages. Although the capacity of eDNA metabarcoding to describe fish assemblages is recognised, research efforts are still needed to better assess the spatial and temporal variability of the eDNA signal and to ultimately design an optimal sampling strategy for eDNA monitoring. In this context, we sampled three different lakes (a dam reservoir, a shallow eutrophic lake and a deep oligotrophic lake) every 6 weeks for 1 year. We performed four types of sampling for each lake (integrative sampling of sub-surface water along transects on the left shore, the right shore and above the deepest zone, and point sampling in deeper layers near the lake bottom) to explore the spatial variability of the eDNA signal at the lake scale over a period of 1 year. A metabarcoding approach was applied to analyse the 92 eDNA samples in order to obtain fish species inventories which were compared with traditional fish monitoring methods (standardized gillnet samplings). Several species known to be present in these lakes were only detected by eDNA, confirming the higher sensitivity of this technique in comparison with gillnetting. The eDNA signal varied spatially, with shoreline samples being richer in species than the other samples. Furthermore, deep-water samplings appeared to be non-relevant for regularly mixed lakes, where the eDNA signal was homogeneously distributed. These results also demonstrate a clear temporal variability of the eDNA signal that seems to be related to species phenology, with most of the species detected in spring during the spawning period on shores, but also a peak of detection in winter for salmonid and coregonid species during their reproduction period. These results contribute to our understanding of the spatio-temporal distribution of eDNA in lakes and allow us to provide methodological recommendations regarding where and when to sample eDNA for fish monitoring in lakes.

Environmental DNA analysis has emerged as a key component of biodiversity and environmental monitoring. However, the state and fate of eDNA in natural environments is still poorly understood for many ecological systems. Here we assess the state and fate of eDNA derived from the water flea, Daphnia magna , using a full factorial mesocosm experiment. We measured the quantity and degradation of eDNA over a two month period across a range of filters differing in pore size (0, 0.2, 1 and 10 µm), which spans the range of eDNA source material including subcellular, cellular and tissue. We also used two primer sets targeting mitochondrial (COI) and nuclear (18S) genomic regions. Our findings demonstrated that eDNA was most prevalent in the effluent water, but also reliably detected on the 0.2 μm filter, suggesting subcellular material is the predominate state of eDNA. Temporal eDNA quantity dynamics followed an exponential decay function over the course of 6-17 days, demonstrating a predictable decline in eDNA concentration. Nuclear eDNA was more abundant than mitochondrial eDNA, which may be a result of greater primer affinity, or indicate greater availability of nuclear eDNA gene targets in the environment. In contrast to two previous size-sorting experiments, which utilizing fish eDNA, our findings suggest that the state of invertebrate eDNA is much smaller than previously suspected. Overall, our data suggest that the detection of eDNA greatly depends on our knowledge of the state and fate of eDNA, which differ among species, and likely across environmental conditions.

Environmental DNA (eDNA) metabarcoding is widely used for species analysis, while the use of environmental RNA (eRNA) metabarcoding is more limited. We conducted comparative eDNA/eRNA metabarcoding of the algae and arthropods (aquatic insects) in water samples from Naka River, Japan, to evaluate their potential for biological monitoring and water quality assessment. Both methods detected various algae and arthropod species; however, their compositions were remarkably different from those in traditional field surveys (TFSs), indicating low sensitivity. For algae, the species composition derived from eDNA and eRNA metabarcoding was equivalent. While TFSs focus on attached algae, metabarcoding analysis theoretically detects both planktonic and attached algae. A recently expanded genomic database for aquatic insects significantly contributed to the sensitivity and positive predictivity for arthropods. While the sensitivity of eRNA was lower than that of eDNA, the positive predictivity of eRNA was higher. The eRNA of terrestrial arthropods indicated extremely high or low read numbers when compared with eDNA, suggesting that eRNA could be an effective indicator of false positives. Arthropod and algae eDNA/eRNA metabarcoding analysis enabled water quality estimates from TFSs. The eRNA of algae and arthropods could thus be used to evaluate biodiversity and water quality and provide insights from ecological surveys.