A step towards the validation of bacteria biotic indices using DNA metabarcoding for benthic monitoring

Aylagas, Eva; Atalah, Javier; Sánchez-Jérez, Pablo; Pearman, John K.; Casado, Nuria; Asensi, Jorge; Toledo-Guedes, Kilian; Carvalho, Susana

doi:10.1111/1755-0998.13395

Cited by 28 publications

(10 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biotic indices based solely on bacterial community profiles are similarly strongly correlated with impacts of salmon aquaculture, and those indices are sufficiently robust to be replicable across multiple independent laboratories [ 92 ], suggesting potential utility for routine biomonitoring. Of particular interest with respect to the current study is the fact that “de novo” methods for identifying bacterial indicators—i.e., taxonomy-free approaches based on a statistical analysis of ASVs—performed better in these contexts than a taxonomy-based approach in which 16S DNA sequence data was used to calculate a previously developed bacterial biotic index [ 93 ]. In addition, complementary analyses associated with our work also found that bacteria-nutrient relationships remained strong over time, except immediately following hydrologic disturbance, and that bacterial metrics best represent long-term nutrient concentrations, though they were still correlated with short-term concentrations as well [ 94 ].…”

Section: Discussionmentioning

confidence: 99%

Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria

Pilgrim

Smucker

et al. 2022

Water

View full text Add to dashboard Cite

Indicators based on nutrient-biota relationships in streams can inform water quality restoration and protection programs. Bacterial assemblages could be particularly useful indicators of nutrient effects because they are species-rich, important contributors to ecosystem processes in streams, and responsive to rapidly changing conditions. Here, we sampled 25 streams weekly (12–14 times each) and used 16S rRNA gene metabarcoding of periphyton-associated bacteria to quantify the effects of total phosphorus (TP) and total nitrogen (TN). Threshold indicator taxa analysis identified assemblage-level changes and amplicon sequence variants (ASVs) that increased or decreased with increasing TP and TN concentrations (i.e., low P, high P, low N, and high N ASVs). Boosted regression trees confirmed that relative abundances of gene sequence reads for these four indicator groups were associated with nutrient concentrations. Gradient forest analysis complemented these results by using multiple predictors and random forest models for each ASV to identify portions of TP and TN gradients at which the greatest changes in assemblage structure occurred. Synthesized statistical results showed bacterial assemblage structure began changing at 24 µg TP/L with the greatest changes occurring from 110 to 195 µg/L. Changes in the bacterial assemblages associated with TN gradually occurred from 275 to 855 µg/L. Taxonomic and phylogenetic analyses showed that low nutrient ASVs were commonly Firmicutes, Verrucomicrobiota, Flavobacteriales, and Caulobacterales, Pseudomonadales, and Rhodobacterales of Proteobacteria, whereas other groups, such as Chitinophagales of Bacteroidota, and Burkholderiales, Rhizobiales, Sphingomonadales, and Steroidobacterales of Proteobacteria comprised the high nutrient ASVs. Overall, the responses of bacterial ASV indicators in this study highlight the utility of metabarcoding periphyton-associated bacteria for quantifying biotic responses to nutrient inputs in streams.

show abstract

Section: Discussionmentioning

confidence: 99%

Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria

Pilgrim

Smucker

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

“…measuring nitrogen cycle genes in soils to monitor the effects of land-use changes [ 29 ]). The use of eDNA data for monitoring has largely focused on taxonomic observations of indicator organisms [ 30 , 31 ]. In the majority of cases, health and function are inferred from the presence/absence or relative abundance of these indicator organisms, with this data then used to inform a response or intervention [ 32 ].…”

Section: Environmental Monitoringmentioning

confidence: 99%

Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience

Beale

Jones

Bose

et al. 2022

Emerging Topics in Life Sciences

View full text Add to dashboard Cite

Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of ‘ecosurveillance’, especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.

show abstract

“…These taxon-independent approaches can out-perform taxonomic indices, particularly for groups where reference data are limited, such as bacteria. For example, Aylagas et al (2021) used a regression approach to assign indicator values to microbial ASVs along a sediment organic enrichment gradient near aquaculture farms, and the taxon-independent index outperformed the taxon-dependent microgAMBI index by improving the discrimination of changes in organic enrichment. To date, taxon-independent indices have been largely developed within individual taxon groups such as diatoms ( Apothéloz-Perret-Gentil et al, 2017 ; Feio et al, 2020 ) and bacteria ( Aylagas et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

TICI: a taxon-independent community index for eDNA-based ecological health assessment

Wilkinson,

Gault,

Welsh

et al. 2024

PeerJ

View full text Add to dashboard Cite

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n = 16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R2 = 0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.

show abstract

A step towards the validation of bacteria biotic indices using DNA metabarcoding for benthic monitoring

Cited by 28 publications

References 71 publications

Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria

Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria

Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience

TICI: a taxon-independent community index for eDNA-based ecological health assessment

Contact Info

Product

Resources

About