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Environmental DNA (eDNA) analysis is a rapid, non-invasive, cost-efficient biodiversity monitoring tool with enormous potential to inform aquatic conservation and management. Development is ongoing, with strong commercial interest, and new uses are continually being discovered. General applications of eDNA and guidelines for best practice in freshwater systems have been established, but habitat-specific assessments are lacking. Ponds are highly diverse, yet understudied systems that could benefit from eDNA monitoring. However, eDNA applications in ponds and methodological constraints specific to these environments remain unaddressed. Following a stakeholder workshop in 2017, researchers combined knowledge and expertise to review these applications and challenges that must be addressed for the future and consistency of eDNA monitoring in ponds. The greatest challenges for pond eDNA surveys are representative sampling, eDNA capture, and potential PCR inhibition. We provide recommendations for sampling, eDNA capture, inhibition testing, and laboratory practice, which should aid new and ongoing eDNA projects in ponds. If implemented, these recommendations will contribute towards an eventual broad standardisation of eDNA research and practice, with room to tailor workflows for optimal analysis and different applications. Such standardisation will provide more robust, comparable, and ecologically meaningful data to enable effective conservation and management of pond biodiversity.

Lotic ecosystems such as rivers and streams are unique in that they represent a continuum of both space and time during the transition from headwaters to the river mouth. As microbes have very different controls over their ecology, distribution and dispersion compared with macrobiota, we wished to explore biogeographical patterns within a river catchment and uncover the major drivers structuring bacterioplankton communities. Water samples collected across the River Thames Basin, UK, covering the transition from headwater tributaries to the lower reaches of the main river channel were characterised using 16S rRNA gene pyrosequencing. This approach revealed an ecological succession in the bacterial community composition along the river continuum, moving from a community dominated by Bacteroidetes in the headwaters to Actinobacteria-dominated downstream. Location of the sampling point in the river network (measured as the cumulative water channel distance upstream) was found to be the most predictive spatial feature; inferring that ecological processes pertaining to temporal community succession are of prime importance in driving the assemblages of riverine bacterioplankton communities. A decrease in bacterial activity rates and an increase in the abundance of low nucleic acid bacteria relative to high nucleic acid bacteria were found to correspond with these downstream changes in community structure, suggesting corresponding functional changes. Our findings show that bacterial communities across the Thames basin exhibit an ecological succession along the river continuum, and that this is primarily driven by water residence time rather than the physico-chemical status of the river.

Biofilm-dwelling microorganisms coat the surfaces of stones in rivers and streams, forming diverse communities that are fundamental to biogeochemical processes and ecosystem functioning. Flowing water (lotic) ecosystems face mounting pressures from changes in land use, chemical pollution, and climate change. Despite their ecological importance, the taxonomic and functional diversity of river biofilms and their responses to environmental change are poorly understood at large spatial scales. We conducted a national-scale assessment of bacterial diversity and function using metagenomic sequencing from rivers and streams across England. We recovered 1,014 metagenome-assembled genomes (MAGs) from 450 biofilms collected across England’s extensive river network. Substantial taxonomic novelty was identified, with ~20% of the MAGs representing novel genera. Here we show that biofilm communities, dominated by generalist bacteria, exhibit remarkable functional diversity and metabolic versatility, and likely play a significant role in nutrient cycling with the potential for contaminant transformation. Measured environmental drivers collectively explained an average of 71% of variation in the relative abundance of bacterial MAGs, with geology and land cover contributing most strongly. These findings highlight the importance of river biofilms and establish a foundation for future research on the roles of biofilms in ecosystem health and resilience to environmental change. Here, the authors conduct a metagenomic-based study of England’s rivers to show that biofilm bacteria are taxonomically and functionally diverse and are key to biogeochemical cycling, highlighting the importance of river biofilm bacteria in understanding and monitoring freshwater ecosystem health.

As central place foragers, bees integrate information over large spatial scales on diet and pollutant exposure, offering insights into environmental impacts on their populations. Data from bee biomonitoring has strong applied and policy relevance, particularly when conducted over extensive spatial and temporal scales. However, practical challenges have limited large-scale sustainable implementation of such monitoring networks beyond relatively small-scale experimental studies. This paper describes the creation of a national, citizen science–led honeybee biomonitoring platform. Citizen scientist beekeepers provide biological samples at a national scale that would be cost prohibitive to replicate using conventional sampling strategies. Environmental DNA (eDNA) within honey allows quantification of spatial and temporal patterns in foraging resources. From 2018–2025, over 3,500 beekeepers have contributed 5,789 honey samples from across England, Wales, Scotland, and Northern Ireland. Most samples are collected between May and October and originate from intensively managed agricultural land (54% land use cover), urban and suburban areas (25%), forests (13%), and extensively managed landscapes (8%). eDNA analyses from 2018–2022 reveal strong temporal and spatial variation in plant resource use. Brassicas (wild and crop species such as oilseed rape), clovers (Trifolium spp.), and brambles (Rubus spp.) dominate honeybee diets, alongside notable use of invasive plants. Large-scale, long-term monitoring of floral resource use by honeybees establishes a benchmark for assessing resource availability to wider pollinator communities. The scheme provides data to interpret land-use change, agri-environmental policy outcomes, and climate-driven shifts in flowering resources. Archived honey samples also support future research on invasive species, bee pathogens, and chemical (including pesticide) exposure. The combination of citizen science and eDNA methods enables cost-effective, nationwide ecological monitoring at a scale unattainable through traditional approaches.

Environmental DNA offers great potential as a biodiversity monitoring tool. Previous work has demonstrated that eDNA metabarcoding provides reliable information for lake fish monitoring, but important questions remain about temporal and spatial repeatability, which is critical for understanding the ecology of eDNA and developing effective sampling strategies. Here, we carried out comprehensive spatial sampling of England's largest lake, Windermere, during summer and winter to (1) examine repeatability of the method, (2) compare eDNA results with contemporary gill‐net survey data, (3) test the hypothesis of greater spatial structure of eDNA in summer compared to winter due to differences in water mixing between seasons, and (4) compare the effectiveness of shore and offshore sampling for species detection. We find broad consistency between the results from three sampling events in terms of species detection and abundance, with eDNA detecting more species than established methods and being significantly correlated with rank abundance determined by long‐term data. As predicted, spatial structure was much greater in the summer, reflecting less mixing of eDNA than in the winter. For example Arctic charr, a deep‐water species, was only detected in deep, midlake samples in the summer, while littoral or benthic species such as minnow and stickleback were more frequently detected in shore samples. By contrast in winter, the eDNA of these species was more uniformly distributed. This has important implications for design of sampling campaigns, for example, deep‐water species could be missed and littoral/benthic species overrepresented by focusing exclusively on shoreline samples collected in the summer.

Plasmids enable the dissemination of antimicrobial resistance (AMR) in common Enterobacterales pathogens, representing a major public health challenge. However, the extent of plasmid sharing and evolution between Enterobacterales causing human infections and other niches remains unclear, including the emergence of resistance plasmids. Dense, unselected sampling is essential to developing our understanding of plasmid epidemiology and designing appropriate interventions to limit the emergence and dissemination of plasmid-associated AMR. We established a geographically and temporally restricted collection of human bloodstream infection (BSI)-associated, livestock-associated (cattle, pig, poultry, and sheep faeces, farm soils) and wastewater treatment work (WwTW)-associated (influent, effluent, waterways upstream/downstream of effluent outlets) Enterobacterales. Isolates were collected between 2008 and 2020 from sites <60 km apart in Oxfordshire, UK. Pangenome analysis of plasmid clusters revealed shared ‘backbones’, with phylogenies suggesting an intertwined ecology where well-conserved plasmid backbones carry diverse accessory functions, including AMR genes. Many plasmid ‘backbones’ were seen across species and niches, raising the possibility that plasmid movement between these followed by rapid accessory gene change could be relatively common. Overall, the signature of identical plasmid sharing is likely to be a highly transient one, implying that plasmid movement might be occurring at greater rates than previously estimated, raising a challenge for future genomic One Health studies.

We use a national citizen science monitoring scheme to quantify how agricultural intensification affects honeybee diet breadth (number of plant species). To do this we used DNA metabarcoding to identify the plants present in 527 honey samples collected in 2019 across Great Britain. The species richness of forage plants was negatively correlated with arable cropping area, although this was only found early in the year when the abundance of flowering plants was more limited. Within intensively farmed areas, honeybee diets were dominated by Brassica crops (including oilseed rape). We demonstrate how the structure and complexity of honeybee foraging relationships with plants is negatively affected by the area of arable crops surrounding hives. Using information collected from the beekeepers on the incidence of an economically damaging bee disease (Deformed Wing Virus) we found that the occurrence of this disease increased where bees foraged in agricultural land where there was a high use of foliar insecticides. Understanding impacts of land use on resource availability is fundamental to assessing long-term viability of pollinator populations. These findings highlight the importance of supporting temporally timed resources as mitigation strategies to support wider pollinator population viability.

Background Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~ 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open-source software pipeline, ‘ResPipe’. Results Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve < 1% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment. Conclusions Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available ( https://gitlab.com/hsgweon/ResPipe ).

Biodiversity surveys are critical for detecting environmental change; however, undertaking them at scale and capturing all available diversity through observation is challenging and costly. This study evaluated the potential of soil‐extracted eDNA to describe plant communities and compared these findings to traditional, observation‐based field surveys. We analyzed 789 soil samples using high‐throughput amplicon sequencing and compared DNA‐based diversity metrics, indicator taxa, predicted vegetation class, and plant cover in a comparison with co‐located field survey data. The results indicated that taxonomically aggregated (genus) eDNA‐derived data, while showing slightly reduced Shannon's diversity scores, yielded remarkably similar overall richness and composition estimates. However, the DNA indicator taxa and predictive power for vegetation community classification were also lower overall than those recorded by the field survey. In many cases, plant cover could be inferred from amplicon abundance data with some accuracy despite widely differing scales of sampling—0.25 g crumb of soil versus a 1 m2 quadrat. Overall, results from eDNA demonstrated lower sensitivity but were broadly in accordance with traditional surveys, with our findings revealing comparable taxonomic resolution at the genus level. We demonstrate the potential and limitations of a simple molecular method to inform landscape‐scale plant biodiversity surveys, a vital tool in the monitoring of land use and environmental change. Biodiversity surveys are essential but challenging to conduct at scale, prompting this study to evaluate soil‐extracted eDNA as a proxy for traditional field‐based surveys for describing plant communities. Analysis of 789 soil samples showed that eDNA data, while slightly less sensitive and predictive, provided comparable taxonomic richness and composition estimates at the genus level. These findings demonstrate the potential of eDNA as a scalable tool for monitoring plant biodiversity, despite its limitations in sensitivity and predictive accuracy compared to conventional methods.

There is an expanding body of evidence that environmental DNA (eDNA) can serve as a reliable alternative to traditional assessments of biodiversity and ecological quality. Riverine benthic ecosystems represent one such habitat, holding significant promise for ecological health evaluations using eDNA. Diatoms have typically been assessed in these environmental biofilms through both molecular and conventional methods. However, a wide diversity of life has not been targeted previously, which may serve as important indicators of water quality. To be fully integrated into existing monitoring programs, it is essential to demonstrate the reliability of eDNA‐based assessments. This entails developing unbiased methodologies that capture total DNA across the entire community. DNA extraction from environmental samples is critical in analyzing microbial communities; nevertheless, current workflows often focus on individual kingdoms or communities. In this study, we investigated how extraction methodologies can bias the analysis of microbial community composition using amplicon sequencing at a cross‐kingdom level in river phytobenthos samples. We tested four commercially available DNA extraction methodologies on 23 freshwater benthic biofilm samples collected across a pH and conductivity gradient. Quantitative PCR and metabarcoding of four amplicons (16S, 18S, ITS, and rbcL), targeting bacterial, eukaryotic, fungal, and phototrophic communities, were employed to assess the impact of the DNA extraction kits on community evaluation. This study revealed a high level of similarity between methods incorporating mechanical lysis, which exhibited higher PCR and sequencing success rates as well as increased cross‐kingdom richness and differential abundance compared to chemical and enzymatic lysis alone. However, the origin of the samples, rather than the extraction methodology, emerged as the most significant factor linking them. We recommend utilizing mechanical lysis to optimize cross‐kingdom recovery from environmental samples. Nonetheless, the strong correlation between sample origin and extraction method implies that existing data gathered through alternative methodologies remain valid for informing future monitoring practices. This work examines how DNA extraction methodology may bias the analysis of community composition using amplicon sequencing at a cross‐kingdom level in river phytobenthos samples. Our overall findings suggest that, irrespective of kit, the application of mechanical lysis is likely to improve low abundance community member detection of microbes. However, the high correlation between sample origin, irrespective of the extraction method employed, would imply that existing data collected using alternative methodologies are still valid to include and inform future monitoring practices.