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Root exudates comprise a large variety of compounds released by plants into the rhizosphere, including low-molecular-weight primary metabolites (particularly saccharides, amino acids and organic acids) and secondary metabolites (phenolics, flavonoids and terpenoids). Changes in exudate composition could have impacts on the plant itself, on other plants, on soil properties (e.g. amount of soil organic matter), and on soil organisms. The effects of drought on the composition of root exudates, however, have been rarely studied. We used an ecometabolomics approach to identify the compounds in the exudates of Quercus ilex (holm oak) under an experimental drought gradient and subsequent recovery. Increasing drought stress strongly affected the composition of the exudate metabolome. Plant exudates under drought consisted mainly of secondary metabolites (71% of total metabolites) associated with plant responses to drought stress, whereas the metabolite composition under recovery shifted towards a dominance of primary metabolites (81% of total metabolites). These results strongly suggested that roots exude the most abundant root metabolites. The exudates were changed irreversibly by the lack of water under extreme drought conditions, and the plants could not recover.

Soil-borne plant diseases are increasingly causing devastating losses in agricultural production. The development of a more refined model for disease prediction can aid in reducing crop losses through the use of preventative control measures or soil fallowing for a planting season. The emergence of high-throughput DNA sequencing technology has provided unprecedented insight into the microbial composition of diseased versus healthy soils. However, a single independent case study rarely yields a general conclusion predictive of the disease in a particular soil. Here, we attempt to account for the differences among various studies and plant varieties using a machine-learning approach based on 24 independent bacterial data sets comprising 758 samples and 22 independent fungal data sets comprising 279 samples of healthy or Fusarium wilt-diseased soils from eight different countries. We found that soil bacterial and fungal communities were both clearly separated between diseased and healthy soil samples that originated from six crops across nine countries or regions. Alpha diversity was consistently greater in the fungal community of healthy soils. While diseased soil microbiomes harbored higher abundances of Xanthomonadaceae , Bacillaceae , Gibberella , and Fusarium oxysporum , the healthy soil microbiome contained more Streptomyces Mirabilis , Bradyrhizobiaceae , Comamonadaceae , Mortierella , and nonpathogenic fungi of Fusarium . Furthermore, a random forest method identified 45 bacterial OTUs and 40 fungal OTUs that categorized the health status of the soil with an accuracy >80%. We conclude that these models can be applied to predict the potential for occurrence of F. oxysporum wilt by revealing key biological indicators and features common to the wilt-diseased soil microbiome.

As environmental DNA (eDNA) studies have grown in popularity for use in ecological applications, it has become clear that their results differ in significant ways from those of traditional, non-PCR-based surveys. In general, eDNA studies that rely on amplicon sequencing may detect hundreds of species present in a sampled environment, but the resulting species composition can be idiosyncratic, reflecting species’ true biomass abundances poorly or not at all. Here, we use a set of simulations to develop a mechanistic understanding of the processes leading to the kinds of results common in mixed-template PCR-based (metabarcoding) studies. In particular, we focus on the effects of PCR cycle number and primer amplification efficiency on the results of diversity metrics in sequencing studies. We then show that proportional indices of amplicon reads capture trends in taxon biomass with high accuracy, particularly where amplification efficiency is high (median correlation up to 0.97). Our results explain much of the observed behavior of PCR-based studies, and lead to recommendations for best practices in the field.

A central challenge in microbial ecology is to understand the underlying mechanisms driving community assembly, particularly in the continuum of species sorting and dispersal limitation. However, little is known about the relative importance of species sorting and dispersal limitation in shaping marine microbial communities; especially, how they are related to organism types/traits and water depth. Here, we used variation partitioning and null model analysis to compare mechanisms driving bacterial and protist metacommunity dynamics at the basin scale in the East China Sea, based on MiSeq paired-end sequencing of 16S ribosomal DNA (rDNA) and 18S rDNA, respectively, in surface, deep chlorophyll maximum and bottom layers. Our analyses indicated that protist communities were governed more strongly by species sorting relative to dispersal limitation than were bacterial communities; this pattern was consistent across the three-depth layers, albeit to different degrees. Furthermore, we detected that bacteria exhibited wider habitat niche breadths than protists, whereas, passive dispersal abilities were not appreciably different between them. Our findings support the ‘size-plasticity’ hypothesis: smaller organisms (bacteria) are less environment filtered than larger organisms (protists), as smaller organisms are more likely to be plastic in metabolic abilities and have greater environmental tolerance.

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.

The use of environmental DNA (eDNA) in biodiversity assessments offers a step-change in sensitivity, throughput and simultaneous measures of ecosystem diversity and function. There remains, however, a need to examine eDNA persistence in the wild through simultaneous temporal measures of eDNA and biota. Here, we use metabarcoding of two markers of different lengths, derived from an annual time series of aqueous lake eDNA to examine temporal shifts in ecosystem biodiversity and in an ecologically important group of macroinvertebrates (Diptera: Chironomidae). The analyses allow different levels of detection and validation of taxon richness and community composition (β-diversity) through time, with shorter eDNA fragments dominating the eDNA community. Comparisons between eDNA, community DNA, taxonomy and UK species abundance data further show significant relationships between diversity estimates derived across the disparate methodologies. Our results reveal the temporal dynamics of eDNA and validate the utility of eDNA metabarcoding for tracking seasonal diversity at the ecosystem scale. DNA from macrobial taxa can be extracted from environmental samples, including water, and be used to assess biodiversity in the region. Here, Bista and colleagues show that temporal shifts in the biodiversity of a lake invertebrate community can be detected by analysis of environmental DNA (eDNA).

Over the last decade, an increasing number of studies have used lake sediment DNA to trace past landscape changes, agricultural activities or human presence. However, the processes responsible for lake sediment formation and sediment properties might affect DNA records via taphonomic and analytical processes. It is crucial to understand these processes to ensure reliable interpretations for “palaeo” studies. Here, we combined plant and mammal DNA metabarcoding analyses with sedimentological and geochemical analyses from three lake-catchment systems that are characterised by different erosion dynamics. The new insights derived from this approach elucidate and assess issues relating to DNA sources and transfer processes. The sources of eroded materials strongly affect the “catchment-DNA” concentration in the sediments. For instance, erosion of upper organic and organo-mineral soil horizons provides a higher amount of plant DNA in lake sediments than deep horizons, bare soils or glacial flours. Moreover, high erosion rates, along with a well-developed hydrographic network, are proposed as factors positively affecting the representation of the catchment flora. The development of open and agricultural landscapes, which favour the erosion, could thus bias the reconstructed landscape trajectory but help the record of these human activities. Regarding domestic animals, pastoral practices and animal behaviour might affect their DNA record because they control the type of source of DNA (“point” vs . “diffuse”).

Antibiotic resistance genes (ARGs) have moved from the environmental resistome into human commensals and pathogens, driven by human selection with antimicrobial agents. These genes have increased in abundance in humans and domestic animals, to become common components of waste streams. Estuarine habitats lie between terrestrial/freshwater and marine ecosystems, acting as natural filtering points for pollutants. Here, we have profiled ARGs in sediments from 18 estuaries over 4,000 km of coastal China using high-throughput quantitative polymerase chain reaction, and investigated their relationship with bacterial communities, antibiotic residues and socio-economic factors. ARGs in estuarine sediments were diverse and abundant, with over 200 different resistance genes being detected, 18 of which were found in all 90 sediment samples. The strong correlations of identified resistance genes with known mobile elements, network analyses and partial redundancy analysis all led to the conclusion that human activity is responsible for the abundance and dissemination of these ARGs. Such widespread pollution with xenogenetic elements has environmental, agricultural and medical consequences. A survey of sediments from 18 estuaries over 4000 km of coastal China reveals diverse and abundant antibiotic resistance genes. Analyses of socio-economic factors suggest that the presence of antibiotic resistance genes correlates with human activity.

Environmental DNA (eDNA) metabarcoding can greatly enhance our understanding of global biodiversity and our ability to detect rare or cryptic species. However, sampling effort must be considered when interpreting results from these surveys. We explored how sampling effort influenced biodiversity patterns and nonindigenous species (NIS) detection in an eDNA metabarcoding survey of four commercial ports. Overall, we captured sequences from 18 metazoan phyla with minimal differences in taxonomic coverage between 18 S and COI primer sets. While community dissimilarity patterns were consistent across primers and sampling effort, richness patterns were not, suggesting that richness estimates are extremely sensitive to primer choice and sampling effort. The survey detected 64 potential NIS, with COI identifying more known NIS from port checklists but 18 S identifying more operational taxonomic units shared between three or more ports that represent un-recorded potential NIS. Overall, we conclude that eDNA metabarcoding surveys can reveal global similarity patterns among ports across a broad array of taxa and can also detect potential NIS in these key habitats. However, richness estimates and species assignments require caution. Based on results of this study, we make several recommendations for port eDNA sampling design and suggest several areas for future research.

More of the diversity present in any microbiome is revealed by a method to sequence 16S rRNA that avoids primer bias. Small subunit ribosomal RNA (SSU rRNA) genes, 16S in bacteria and 18S in eukaryotes, have been the standard phylogenetic markers used to characterize microbial diversity and evolution for decades. However, the reference databases of full-length SSU rRNA gene sequences are skewed to well-studied ecosystems and subject to primer bias and chimerism, which results in an incomplete view of the diversity present in a sample. We combine poly(A)-tailing and reverse transcription of SSU rRNA molecules with synthetic long-read sequencing to generate high-quality, full-length SSU rRNA sequences, without primer bias, at high throughput. We apply our approach to samples from seven different ecosystems and obtain more than a million SSU rRNA sequences from all domains of life, with an estimated raw error rate of 0.17%. We observe a large proportion of novel diversity, including several deeply branching phylum-level lineages putatively related to the Asgard Archaea. Our approach will enable expansion of the SSU rRNA reference databases by orders of magnitude, and contribute to a comprehensive census of the tree of life.