Microbial assemblages and bioindicators as proxies for ecosystem health status: potential and limitations

Astudillo‐García, Carmen; Hermans, Syrie M.; Stevenson, Bryan; Buckley, Hannah; Lear, Gavin

doi:10.1007/s00253-019-09963-0

Cited by 59 publications

(37 citation statements)

References 131 publications

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“…The globally biogeographic microbial assemblages could have potential to be efficient biological indicators for the environmental health of the complex natural systems (Astudillo-Garcia et al, 2019). Therefore, revealing microbial biogeography and mechanisms underlying community assembly is a central issue in microbial ecology (Nemergut et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Soil pH and temperature regulate assembly processes of abundant and rare bacterial communities in agricultural ecosystems

Jiao

2019

Environmental Microbiology

269

173

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Summary The factors determining stochastic and deterministic processes that drive microbial community structure, specifically the balance of abundant and rare bacterial taxa, remain underexplored. Here we examined biogeographic patterns of abundant and rare bacterial taxa and explored environmental factors influencing their community assembly processes in agricultural fields across eastern China. More phylogenetic turnover correlating with spatial distance was observed in abundant than rare sub‐communities. Homogeneous selection was the main assembly process for both the abundant and rare sub‐communities; however, the abundant sub‐community was more tightly clustered phylogenetically and was more sensitive to dispersal limitations than the rare sub‐community. Rare sub‐community of rice fields and abundant sub‐community of maize fields were more governed by stochastic assembly processes, which showed higher operational taxonomic unit richness. We propose a conceptual paradigm wherein soil pH and mean annual temperature mediate the assembly of the abundant and rare sub‐communities respectively. A higher soil pH leads to deterministic assembly of the abundant sub‐community. For the rare sub‐community, the dominance of stochasticity in low‐temperature regions indicates weaker niche‐based exclusion and the arrival of more evolutionary lineages. These findings suggest that the community assembly processes for abundant and rare bacterial taxa are dependent on distinct environmental variables in agro‐ecosystems.

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Section: Discussionmentioning

confidence: 99%

Soil pH and temperature regulate assembly processes of abundant and rare bacterial communities in agricultural ecosystems

Jiao

2019

Environmental Microbiology

269

173

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“…Most of the other OTUs are from the Phyla Bacteroidetes and Proteobacteria. Many of the lowest distinct taxa we identified have previously been discussed as bioindicators for general ecosystem quality (Ignavibacteriales (Cordier, 2019), Limnobacter (Yang et al, 2019), and Sandaracinaceae (Wei et al, 2019)), certain environmental variables ( Opitutus (Puranik et al, 2016; Plassart et al, 2019), Alcaligenaceae (Sharuddin et al, 2017), Novosphingobium (Astudillo‐García et al, 2019; Reis et al, 2020), and NS11‐12 marine group (Coclet et al, 2019; Henson et al, 2018)), and human interference/impact/pollution ( Actibacter (Kegler et al, 2018), Fluviicola (Chen et al, 2019), and SC‐I‐84 (Pershina et al, 2015)). However, not all of these taxa have previously been identified in lake ecosystems, and most of the OTUs among these bacterial multitask bioindicators are assigned to taxa originally isolated from soil ecosystems (see Table 3).…”

Section: Resultsmentioning

confidence: 92%

Quantification of the covariation of lake microbiomes and environmental variables using a machine learning‐based framework

et al. 2021

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“…Most of the other OTUs are from the Phyla Bacteroidetes and Proteobacteria. Many of the lowest distinct taxa we identified have previously been discussed as bioindicators for general ecosystem quality (Ignavibacteriales [36], Limnobacter [64], and Sandaracinaceae [65]), certain environmental parameters (Opitutus [17,66], Alcaligenaceae [67], Novosphingobium [68,69], and NS11-12 marine group [70,71]), and human interference/impact/pollution (Actibacter [72], Fluviicola [73], and SC-I-84 [74]). However, not all of these taxa have previously been identified in lake ecosystems, and most of the OTUs among these bacterial multi-task bioindicators are assigned to taxa originally isolated from soil ecosystems (see table 3).…”

Section: Analysis Of Microbial Multi-task Bioindicatorsmentioning

confidence: 94%

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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Microbial assemblages and bioindicators as proxies for ecosystem health status: potential and limitations

Cited by 59 publications

References 131 publications

Soil pH and temperature regulate assembly processes of abundant and rare bacterial communities in agricultural ecosystems

Soil pH and temperature regulate assembly processes of abundant and rare bacterial communities in agricultural ecosystems

Quantification of the covariation of lake microbiomes and environmental variables using a machine learning‐based framework

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

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