Rewetting of three drained peatlands drives congruent compositional changes in pro- and eukaryotic microbiomes through environmental filtering
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB35436 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3668040.fastq.gz | Fastqsanger.gz | |||
| ERR3668050.fastq.gz | Fastqsanger.gz | |||
| ERR3668060.fastq.gz | Fastqsanger.gz | |||
| ERR3668070.fastq.gz | Fastqsanger.gz | |||
| ERR3668080.fastq.gz | Fastqsanger.gz |
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Project description:Drained peatlands are significant sources of the greenhouse gas (GHG) carbon dioxide. Rewetting is a proven strategy used to protect carbon stocks; however, it can lead to increased emissions of the potent GHG methane. The response to rewetting of soil microbiomes as drivers of these processes is poorly understood, as are the biotic and abiotic factors that control community composition. We analyzed the pro- and eukaryotic microbiomes of three contrasting pairs of minerotrophic fens subject to decade-long drainage and subsequent long-term rewetting. Abiotic soil properties including moisture, dissolved organic matter, methane fluxes, and ecosystem respiration rates were also determined. The composition of the microbiomes was fen-type-specific, but all rewetted sites showed higher abundances of anaerobic taxa compared to drained sites. Based on multi-variate statistics and network analyses, we identified soil moisture as a major driver of community composition. Furthermore, salinity drove the separation between coastal and freshwater fen communities. Methanogens were more than 10-fold more abundant in rewetted than in drained sites, while their abundance was lowest in the coastal fen, likely due to competition with sulfate reducers. The microbiome compositions were reflected in methane fluxes from the sites. Our results shed light on the factors that structure fen microbiomes via environmental filtering.
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Project description:Recovery of methanogenic community and its activity in long-term drained peatlands after their rewetting
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Project description:Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of full communities. LDA provides a lens into the microbiome at broad and fine-grained taxonomic levels, which we show on two datasets. In the first dataset, from the literature, we show how LDA topics succinctly recapitulate many results from a previous study on diseased coral species. We then apply LDA to a new dataset of maize soil microbiomes under drought, and find a large number of significant associations between the microbiome topics and plant traits as well as associations between the microbiome and the experimental factors, e.g. watering level. This yields new information on the plant-microbial interactions in maize and shows that LDA technique is useful for studying the coupling between microbiomes and stressed organisms. Author summary Host-microbe interaction may be an important factor determining the performance and survival of an organism under stress. Understanding how microbiomes influence organisms under stress is a challenging new area of research because microbiomes are complex with the potential for complex responses and adaptations to stress that influence their interactions with the other stressed organisms. We show the use of LDA, a data-science technique in the context of environmental microbial datasets to break down the thousands of microbes present in samples into groups called topics; each topic is a group of organisms that occur together as a common pattern in the dataset. We show that this technique, combined with correlation analyses, provides a way to view a very large set of microbes in a sample as a smaller, more manageable set of communities of microbial taxa related to experimental conditions and plant traits. In this way, LDA helps to unravel complex interactions between organisms and their microbiome, which could help to better predict the behavior of real-world ecological systems, and perhaps support them through many challenges brought by changing climate and environment.
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Project description:1. The environmental filtering hypothesis predicts that the abiotic environment selects species with similar trait values within communities. Testing this hypothesis along multiple - and interacting - gradients of climate and soil variables constitutes a great opportunity to better understand and predict the responses of plant communities to ongoing environmental changes. 2. Based on two key plant traits, maximum plant height and specific leaf area (SLA), we assessed the filtering effects of climate (mean annual temperature and precipitation, precipitation seasonality), soil characteristics (soil pH, sand content and total phosphorus) and all potential interactions on the functional structure and diversity of 124 dryland communities spread over the globe. The functional structure and diversity of dryland communities were quantified using the mean, variance, skewness and kurtosis of plant trait distributions. 3. The models accurately explained the observed variations in functional trait diversity across the 124 communities studied. All models included interactions among factors, i.e. climate - climate (9% of explanatory power), climate - soil (24% of explanatory power) and soil - soil interactions (5% of explanatory power). Precipitation seasonality was the main driver of maximum plant height, and interacted with mean annual temperature and precipitation. Soil pH mediated the filtering effects of climate and sand content on SLA. Our results also revealed that communities characterized by a low variance can also exhibit low kurtosis values, indicating that functionally contrasting species can co-occur even in communities with narrow ranges of trait values. 4. Synthesis We identified the particular set of conditions under which the environmental filtering hypothesis operates in drylands worldwide. Our findings also indicate that species with functionally contrasting strategies can still co-occur locally, even under prevailing environmental filtering. Interactions between sources of environmental stress should be therefore included in global trait-based studies, as this will help to further anticipate where the effects of environmental filtering will impact plant trait diversity under climate change.
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