Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes

Frühe, Larissa; Cordier, Tristan; Dully, Verena; Breiner, Hans‐Werner; Lentendu, Guillaume; Pawłowski, Jan; Martins, Catarina I.M.; Wilding, Thomas A.; Stoeck, Thorsten

doi:10.1111/mec.15434

Cited by 67 publications

(65 citation statements)

References 98 publications

(179 reference statements)

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“…Recent studies have argued for differences in ecological function between bacteria and microbial eukaryotes in lake ecosystems [42,89,90]. More specifically, it has been argued that bacteria are more responsive to environmental changes than eukaryotes [14,18,20,42]. This is supported by our result that bacteria that are multi-task bioindicators can be indicative of more lake parameters than eukaryotic multi-task bioindicators (see figure 3).…”

Section: Discussionsupporting

confidence: 79%

“…This is the case since organisms will only emerge as indicative for parameters they react to directly (because of their ecological niche) or indirectly (since they interact closely with organisms that are, in turn, reactive to changes in the respective parameter). Due to their functional diversity, high growth rates, large population sizes, and high surface-to-volume ratio, bacteria and microeukaryotes are very responsive to environmental changes and represent optimal bioindicators [14,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…The advent of next-generation sequencing (NGS) has greatly facilitated the use of microbial bioindicators. Firstly, it made it possible to identify organisms based on their genetic makeup instead of visual features [20,21]. Secondly, techniques such as amplicon sequencing have made it feasible to capture microbial community compositions present in environmental samples [22].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the information content of lake microbiomes using a machine learning-based framework

Sperlea¹,

Kreuder²,

Beißer³

et al. 2020

Preprint

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Background: Bacteria and microbial eukaryotes occupy a wide range of ecological niches and are essential for the functioning of ecosystems. The advent of next-generation sequencing methods enabled the study of environmental microbial community compositions. Yet, many questions regarding the stability and functioning of environmental microbiomes remain open. Results: In the current study, we present a methodological framework to quantify the information shared between the microbial community of a habitat and the abiotic parameters of this habitat. It is built on theoretical considerations of systems ecology and makes use of state-of-the-art machine learning techniques. It can also be used to identify bioindicators. We apply the framework to a dataset containing operational taxonomic units (OTUs) as well as more than twenty physico-chemical and geographic parameters measured in a large-scale survey of European lakes. While a large part of variation (up to 61\%) in many physico-chemical parameters can be explained by microbial community composition, some of the examined parameters only share little information with the microbiome. Moreover, we have identified OTUs that act as `multi-task’ bioindicators that could be potential candidates for lake water monitoring schemes. Conclusions: This study demonstrates the benefits of machine learning approaches in microbial ecology. Our results represent, for the first time, a quantification of information shared between the lake microbiome and a wide array of ecosystem parameters. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes central for the functioning and stability of lake ecosystems.

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Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying the information content of lake microbiomes using a machine learning-based framework

Sperlea¹,

Kreuder²,

Beißer³

et al. 2020

Preprint

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“…The predictions obtained from metazoan-assigned OTUs had kappa values between 0.211 and 0.569, whereas the SML models had kappa values raging from 0.755 to 0.881. Following on from this study, Frühe et al (2020) [92] compared the performance of SML with standard IndVal approach for prediction of environmental status. The indicator species approach directly from OTUs/ASVs has appeal due to there not being a need to assign taxonomy to the OTUs/ASVs like in the metazoan-assigned OTUs approach.…”

Section: Optimizing Model Construction and Evaluationmentioning

confidence: 99%

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Ghannam

Techtmann

2021

Computational and Structural Biotechnology Journal

160

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“…Morphological approaches, however, are often time-consuming, expensive, and require a high level of taxonomic expertise that is shrinking globally ( Jones, 2008 ). These limitations have led to numerous metabarcoding investigations describing the ecological responses of a wide range of organisms associated with enrichment states, including bacteria ( Fodelianakis et al, 2015 ; Dowle et al, 2015 ; Verhoeven et al, 2018 ; Stoeck et al, 2018a ), foraminifera ( He et al, 2010 ; Pawlowski et al, 2014 , 2016a ; Pochon et al, 2015 ), ciliates ( Stoeck et al, 2018b ), metazoans ( Lejzerowicz et al, 2015 ), or a combination of multi-trophic taxa ( Keeley, Wood & Pochon, 2018 ; Frühe et al, 2020 ). Although all of these studies have revealed consistent organismal responses to fish farm enrichment, indicating that metabarcoding is a cost-effective tool for routine monitoring, they have all used different sediment collection methods, varying amounts of starting material (from 0.25 g to 10 g of sediment), and a variety of DNA extraction kits.…”

Section: Introductionmentioning

confidence: 99%

Comparing sediment DNA extraction methods for assessing organic enrichment associated with marine aquaculture

Pearman

Keeley

Wood

et al. 2020

PeerJ

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Marine sediments contain a high diversity of micro- and macro-organisms which are important in the functioning of biogeochemical cycles. Traditionally, anthropogenic perturbation has been investigated by identifying macro-organism responses along gradients. Environmental DNA (eDNA) analyses have recently been advocated as a rapid and cost-effective approach to measuring ecological impacts and efforts are underway to incorporate eDNA tools into monitoring. Before these methods can replace or complement existing methods, robustness and repeatability of each analytical step has to be demonstrated. One area that requires further investigation is the selection of sediment DNA extraction method. Environmental DNA sediment samples were obtained along a disturbance gradient adjacent to a Chinook (Oncorhynchus tshawytscha) salmon farm in Otanerau Bay, New Zealand. DNA was extracted using four extraction kits (Qiagen DNeasy PowerSoil, Qiagen DNeasy PowerSoil Pro, Qiagen RNeasy PowerSoil Total RNA/DNA extraction/elution and Favorgen FavorPrep Soil DNA Isolation Midi Kit) and three sediment volumes (0.25, 2, and 5 g). Prokaryotic and eukaryotic communities were amplified using primers targeting the 16S and 18S ribosomal RNA genes, respectively, and were sequenced on an Illumina MiSeq. Diversity and community composition estimates were obtained from each extraction kit, as well as their relative performance in established metabarcoding biotic indices. Differences were observed in the quality and quantity of the extracted DNA amongst kits with the two Qiagen DNeasy PowerSoil kits performing best. Significant differences were observed in both prokaryotes and eukaryotes (p < 0.001) richness among kits. A small proportion of amplicon sequence variants (ASVs) were shared amongst the kits (~3%) although these shared ASVs accounted for the majority of sequence reads (prokaryotes: 59.9%, eukaryotes: 67.2%). Differences were observed in the richness and relative abundance of taxonomic classes revealed with each kit. Multivariate analysis showed that there was a significant interaction between “distance” from the farm and “kit” in explaining the composition of the communities, with the distance from the farm being a stronger determinant of community composition. Comparison of the kits against the bacterial and eukaryotic metabarcoding biotic index suggested that all kits showed similar patterns along the environmental gradient. Overall, we advocate for the use of Qiagen DNeasy PowerSoil kits for use when characterizing prokaryotic and eukaryotic eDNA from marine farm sediments. We base this conclusion on the higher DNA quality values and richness achieved with these kits compared to the other kits/amounts investigated in this study. The additional advantage of the PowerSoil Kits is that DNA extractions can be performed using an extractor robot, offering additional standardization and reproducibility of results.

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Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 67 publications

References 98 publications

Quantifying the information content of lake microbiomes using a machine learning-based framework

Quantifying the information content of lake microbiomes using a machine learning-based framework

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Comparing sediment DNA extraction methods for assessing organic enrichment associated with marine aquaculture

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