Predicting Hydrologic Function With Aquatic Gene Fragments

Good, Stephen P.; URycki, Dawn; Crump, Byron C.

doi:10.1002/2017wr021974

Cited by 19 publications

(14 citation statements)

References 22 publications

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“…The diagnostic potential of microbial communities, especially in combination with machine learning approaches, has gained momentum across multiple research fields, including disease identification by characterisation of the human gut-microbiome (50), evaluation of the environment and host genetics on the human microbiome (51), prediction of hydrological functions in riverine ecosystems (52) and assessment of macroecological patterns in soil samples (53). This development of microbial-based diagnostics is largely due to availability of high-throughput sequencing of the 16S rRNA gene and streamlined analytical pipelines that facilitate rapid assessment of microbial community composition (54, 55).…”

Section: Discussionmentioning

confidence: 99%

Microbial predictors of environmental perturbations in coral reef ecosystems

Glasl

Bourne

Frade

et al. 2019

Preprint

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22times less affected by the environment and their community assembly patterns were 34 generally less uniform. Further, seawater microbial community data provided an 35 accurate prediction on the environmental state, highlighting the diagnostic value of 36 microorganisms and illustrating how long-term coral reef monitoring initiatives could 37 be enhanced by incorporating assessments of microbial communities in seawater. 38

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Section: Discussionmentioning

confidence: 99%

Microbial predictors of environmental perturbations in coral reef ecosystems

Glasl

Bourne

Frade

et al. 2019

Preprint

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“…Compared to a synthetic tracer, the metabarcoding approach exposes an enormous quantity of information contained in the DNA that opens the door for machine learning that does not account for physical processes (Good et al, 2018). On the other hand, it is quite challenging to identify individual species which would allow for a physical interpretation of the individual flow paths based on individual distributions and habitat preferences (Carraro et al, 2018).…”

Section: Recommendations and Future Stepsmentioning

confidence: 99%

“…eDNA can detect communities of a broad taxonomic scale with the use of a single sampling technique in combination with the high through-put sequencing of a barcoding region (so called metabarcoding), which allows detection of genetic diversity approximately at the level of species. Artificially introduced DNA attached to particles has already been shown to be a reliable and useful hydrologic tracer (Dahlke et al, 2015;McNew et al, 2018;Foppen et al, 2013) and naturally occurring DNA has just started to enter into the repertoire of hydrologic tracers (Good et al, 2018;Carraro et al, 2018). We believe that eDNA metabarcoding holds enormous potential inform analysis of hydrologic flow paths and connectivity from multiple disciplinary perspectives.…”

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confidence: 95%

Water tracing with environmental DNA in a high-Alpine catchment

Mächler

Salyani

Walser

et al. 2019

Preprint

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Abstract. Alpine streams are particularly valuable for downstream water resources and for ecosystem conservation. However, the details of where and when water is stored and released in the heterogeneous mountain environment are rarely known. The use of physico-chemical flow path tracers is particularly challenging due to the temporary accumulation and storage of water in the form of snow and ice. Alternatively, biological tracers might complement information on flow and storage of water, especially as the different microhabitats in Alpine aquatic systems are inhabited by characteristic organismal communities. In this study, we explored the potential of particles of environmental DNA found in the water (eDNA) to characterize hydrological flow paths and connectivity in an Alpine catchment in Switzerland. Between March and September 2017, we sampled water at multiple time points at 11 sites distributed over the 13.4 km2 Vallon de Nant catchment for genetic species information based on naturally occurring eDNA. The sites correspond to three different water source types and habitats (main channel, tributaries, and springs). Comparison of typical hydrological tracers and eDNA with temporal evolution of streamflow revealed that in the main channel and in the tributaries, the change in streamflow, dq/dt, is strongly correlated with biological richness. In springs, electrical conductivity was found to have a positive but not as strong correlation with biological richness. At the catchment scale, our results show that biological richness as indicated by the diversity detected by eDNA samples. When streamflow is increasing, transport of additional, and probably terrestrial, DNA into water storage or flow compartments is occurring. Such processes include overbank flow, stream network expansion and retraction, and hyporheic exchange. In general, our results highlight the importance of considering the at-site sampling habitat in combination with upstream connected habitats to understand how streams integrate eDNA over a catchment and to interpret spatially distributed eDNA samples, both for hydrological and biodiversity assessments. We identify next steps to be addressed to use eDNA as an independent tracer of Alpine water sources and we provide recommendations for future observation of eDNA in Alpine stream ecosystems.

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“…Advances in high throughput sequencing of environmental samples have dramatically expanded our knowledge of microbial biodiversity [ 1 ] and present substantial opportunities for the environmental sciences. For example, the diversity and composition of the microbial community may be useful as direct or indirect proxies for assessing ecosystem condition and health and have recently been used to predict hydrologic function in large Arctic rivers [ 2 ]. Microbe-based applications for ecosystem monitoring and assessment are particularly exciting for classifying the condition of freshwater streams because a small, easily collected sample could augment or replace the substantial efforts required for traditional methods based on eukaryotes [e.g., 3 ].…”

Section: Introductionmentioning

confidence: 99%

Microbial communities can predict the ecological condition of headwater streams

et al. 2020

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Humanity’s reliance on clean water and the ecosystem services provided makes identifying efficient and effective ways to assess the ecological condition of streams ever more important. We used high throughput sequencing of the 16S rRNA region to explore relationships between stream microbial communities, environmental attributes, and assessments of stream ecological condition. Bacteria and archaea in microbial community samples collected from the water column and from stream sediments during spring and summer were used to replicate standard assessments of ecological condition performed with benthic macroinvertebrate collections via the Benthic Index of Biotic Integrity (BIBI). Microbe-based condition assessments were generated at different levels of taxonomic resolution from phylum to OTU (Operational Taxonomic Units) in order to understand appropriate levels of taxonomic aggregation. Stream sediment microbial communities from both spring and summer were much better than the water column at replicating BIBI condition assessment results. Accuracies were as high as 100% on training data used to build the models and up to 80% on validation data used to assess predictions. Assessments using all OTUs usually had the highest accuracy on training data, but were lower on validation data due to overfitting. In contrast, assessments at the order-level had similar performance accuracy for validation data, and a reduced subset of orders also performed well, suggesting the method could be generalized to other watersheds. Subsets of the important orders responded similarly to environmental gradients compared to the entire community, where strong shifts in community structure occurred for known aquatic stressors such as pH, dissolved organic carbon, and nitrate nitrogen. The results suggest the stream microbes may be useful for assessing the ecological condition of streams and especially useful for stream restorations where many eukaryotic taxa have been eliminated due to prior degradation and are unable to recolonize.

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Predicting Hydrologic Function With Aquatic Gene Fragments

Cited by 19 publications

References 22 publications

Microbial predictors of environmental perturbations in coral reef ecosystems

Microbial predictors of environmental perturbations in coral reef ecosystems

Water tracing with environmental DNA in a high-Alpine catchment

Microbial communities can predict the ecological condition of headwater streams

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