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Data from: Drought mitigates negative effects of natural microbiomes in grasses

Abstract: Research suggests that soil microbes, specifically arbuscular mycorrhizal fungi (AMFs), can help mitigate plant drought stress, but their beneficial effects on grasses are unclear. Here we test the hypothesis that soil microbes alleviate drought's negative effects on grass growth. In a greenhouse experiment, we used natural microbial inocula and eight grass species from various positions along an ecological succession gradient. Under normal watering, live soil communities decreased grass biomass, indicating a net pathogenic effect. However, this negative effect was reduced under drought compared to well-watered conditions. Amplicon sequencing revealed a significant impact of drought on rhizosphere microbial communities, particularly fungi. However, we found no evidence linking AMFs to drought stress alleviation in grasses. Our study suggests that drought reduces the negative effects of soil microbes on grass performance, regardless of successional positions. Our results also indicate that the beneficial effects of AMFs under drought are not universal, and soil biota's role in shaping plant-soil feedbacks may be less pronounced under drought conditions. Data sets description: <ul> <li>Plant biomass_final.xlsx: The experiment was performed in a climatised greenhouse at 16/8 h light/dark and 20/15 °C Day/night conditions. In each pot, three plants were planted in monocultures according to a full randomized block design with eight blocks, 2 watering levels (control, drought), 2 inoculum types (agricultural, late-successional), 2 microbial conditions (sterilized, live) and 8 grass species. Each block contained one replicate of the treatment groups for a total of 64 pots per block. During the first four weeks of the experiment, the soil moisture content of all pots was maintained at 15% (w/w) by watering 2 times per week the pots to compensate unequal losses. After four weeks, we applied the drought treatment where half of the pots were maintained at 7.5% (w/w) by watering the pots to weight for three weeks. After seven weeks of plant growth, shoots were clipped, dried at 60 °C until constant weight, and weighed, whereas roots were first washed and then dried at 60 °C, and weighed to determine biomass.</li> <li>Bacteria data.xlsx: The raw 16S sequence reads were processed using Dada2 (v. 1.12) (Callahan et al. 2016) using default parameters. SILVA (v.132) database was used to classify bacteria. All reads not belonging to bacterial or fungal kingdoms were excluded from the datasets. The analysis was conducted excluding the samples that had "sterilized" microbial treatment (see sample data).</li> <li>Fungi data.xlsx: The raw ITS sequence reads were processed using Pipits (v. 2.3) pipeline (Gweon et al. 2015) using default parameters. The  UNITE (v. 8.0;) database (Abarenkov et al. 2010) was used for the identification of fungi, and the ITSx extractor was used to extract fungal ITS regions. The classification of fungal operational taxonomic units (OTUs) into potential functions was done using FUNGuild (v.1.1.; (Nguyen et al. 2016) and the assignment was further curated using an in-house database (Hannula et al. 2017). The OTUs were grouped into saprotrophs, plant pathogens, plant endophytes and others (i.e., fungal/animal-plant pathogens). Multiple assignments were included in case of uncertain fungal guilds. All reads not belonging to fungal kingdoms were excluded from the datasets. When analyzing fungi, the genus Penicillum was identified as a suspected contaminant in some samples and thus it was excluded from the analysis. SILVA (v.132) database was used to classify bacteria whereas the UNITE (v. 8.0;) database (Abarenkov et al. 2010) was used for the identification of fungi, and the ITSx extractor was used to extract fungal ITS regions. The classification of fungal operational taxonomic units (OTUs) into potential functions was done using FUNGuild (v.1.1.; (Nguyen et al. 2016) and the assignment was further curated using an in-house database (Hannula et al. 2017). The OTUs were grouped into saprotrophs, plant pathogens, plant endophytes and others (i.e., fungal/animal-plant pathogens). Multiple assignments were included in case of uncertain fungal guilds. All reads not belonging to bacterial or fungal kingdoms were excluded from the datasets. When analyzing fungi, the genus Penicillum was identified as a suspected contaminant in some samples and thus it was excluded from the analysis. The classification of fungal operational taxonomic units (OTUs) into potential functions was done using FUNGuild (v.1.1.; (Nguyen et al. 2016) and the assignment was further curated using an in-house database (Hannula et al. 2017). The OTUs were grouped into saprotrophs, plant pathogens, plant endophytes and others (i.e., fungal/animal-plant pathogens). Multiple assignments were included in case of uncertain fungal guilds. All reads not belonging to bacterial or fungal kingdoms were excluded from the datasets. When analyzing fungi, the genus Penicillum was identified as a suspected contaminant in some samples and thus it was excluded from the analysis. The OTUs were grouped into saprotrophs, plant pathogens, plant endophytes and others (i.e., fungal/animal-plant pathogens). Multiple assignments were included in case of uncertain fungal guilds. All reads not belonging to bacterial or fungal kingdoms were excluded from the datasets. When analyzing fungi, the genus Penicillum was identified as a suspected contaminant in some samples and thus it was excluded from the analysis. The genus Penicillum was identified as a suspected contaminant in some samples and thus it was excluded from the analysis. The analysis was conducted excluding the samples that had "sterilized" microbial treatment (see sample data).</li> </ul>

opencc-by-4.0Dec 2023View details →

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Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

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Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

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neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record