Dataset Information

Linking microbial co-occurrences to soil ecological processes across a woodland-grassland ecotone.

ABSTRACT: Ecotones between distinct ecosystems have been the focus of many studies as they offer valuable insights into key drivers of community structure and ecological processes that underpin function. While previous studies have examined a wide range of above-ground parameters in ecotones, soil microbial communities have received little attention. Here we investigated spatial patterns, composition, and co-occurrences of archaea, bacteria, and fungi, and their relationships with soil ecological processes across a woodland-grassland ecotone. Geostatistical kriging and network analysis revealed that the community structure and spatial patterns of soil microbiota varied considerably between three habitat components across the ecotone. Woodland samples had significantly higher diversity of archaea while the grassland samples had significantly higher diversity of bacteria. Microbial co-occurrences reflected differences in soil properties and ecological processes. While microbial networks were dominated by bacterial nodes, different ecological processes were linked to specific microbial guilds. For example, soil phosphorus and phosphatase activity formed the largest clusters in their respective networks, and two lignolytic enzymes formed joined clusters. Bacterial ammonia oxidizers were dominant over archaeal oxidizers and showed a significant association (p < 0.001) with potential nitrification (PNR), with the PNR subnetwork being dominated by Betaproteobacteria. The top ten keystone taxa comprised six bacterial and four fungal OTUs, with Random Forest Analysis revealing soil carbon and nitrogen as the determinants of the abundance of keystone taxa. Our results highlight the importance of assessing interkingdom associations in soil microbial networks. Overall, this study shows how ecotones can be used as a model to delineate microbial structural patterns and ecological processes across adjoining land-uses within a landscape.

SUBMITTER: Banerjee S

PROVIDER: S-EPMC6145019 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Linking microbial co-occurrences to soil ecological processes across a woodland-grassland ecotone.

Banerjee Samiran S Thrall Peter H PH Bissett Andrew A van der Heijden Marcel G A MGA Richardson Alan E AE

Ecology and evolution 20180722 16

Ecotones between distinct ecosystems have been the focus of many studies as they offer valuable insights into key drivers of community structure and ecological processes that underpin function. While previous studies have examined a wide range of above-ground parameters in ecotones, soil microbial communities have received little attention. Here we investigated spatial patterns, composition, and co-occurrences of archaea, bacteria, and fungi, and their relationships with soil ecological processe ...[more]

PMID: 30250697

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Project description:BackgroundGrowth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection.ResultsWe implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell's neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self-organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model.ConclusionsWe present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis.

Dataset Information

Linking microbial co-occurrences to soil ecological processes across a woodland-grassland ecotone.

Publications

Linking microbial co-occurrences to soil ecological processes across a woodland-grassland ecotone.

Similar Datasets

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