Ecological memory of recurrent drought modifies soil processes via changes in soil microbial community.
Ontology highlight
ABSTRACT:
SUBMITTER: Canarini A
PROVIDER: S-EPMC8421443 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Drought imposes stress on plants and associated soil microbes, inducing coordinated adaptive responses, which can involve plant-soil signalling via phytohormones. However, we know little about how microbial communities respond to phytohormones, or how these responses are shaped by chronic (long-term) drought. Here, we added three phytohormones (abscisic acid, 1-aminocyclopropane-1-carboxylic acid, and jasmonic acid) to soils from long-term (25-year), field-based climate treatments to test the hypothesis that chronic drought alters soil microbial community responses to plant stress signalling. Phytohormone addition increased soil respiration, but this effect was stronger in irrigated than in droughted soils and increased soil respiration at low phytohormone concentrations could not be explained by their use as substrate. Thus, we show that drought adaptation within soil microbial communities modifies their responses to phytohormone inputs. Furthermore, distinct phytohormone-induced shifts in microbial functional groups in droughted vs. irrigated soils might suggest that drought-adapted soil microorganisms perceive phytohormones as stress-signals, allowing them to anticipate impending drought.
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Project description:Ecological community assembly is governed by a combination of (i) selection resulting from among-taxa differences in performance; (ii) dispersal resulting from organismal movement; and (iii) ecological drift resulting from stochastic changes in population sizes. The relative importance and nature of these processes can vary across environments. Selection can be homogeneous or variable, and while dispersal is a rate, we conceptualize extreme dispersal rates as two categories; dispersal limitation results from limited exchange of organisms among communities, and homogenizing dispersal results from high levels of organism exchange. To estimate the influence and spatial variation of each process we extend a recently developed statistical framework, use a simulation model to evaluate the accuracy of the extended framework, and use the framework to examine subsurface microbial communities over two geologic formations. For each subsurface community we estimate the degree to which it is influenced by homogeneous selection, variable selection, dispersal limitation, and homogenizing dispersal. Our analyses revealed that the relative influences of these ecological processes vary substantially across communities even within a geologic formation. We further identify environmental and spatial features associated with each ecological process, which allowed mapping of spatial variation in ecological-process-influences. The resulting maps provide a new lens through which ecological systems can be understood; in the subsurface system investigated here they revealed that the influence of variable selection was associated with the rate at which redox conditions change with subsurface depth.
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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.
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Project description:Ecological processes shaping the structure and diversity of microbial communities are of practical importance for managing the function and resilience of engineered biological ecosystems such as activated sludge processes. This study systematically evaluated the ecological processes acting during continuous exposure to a subinhibitory level of antimicrobial triclosan (TCS) as an environmental stressor. 16S rRNA gene-based community profiling revealed significant perturbations on the community structure and dramatic reduction (by 20-30%) in species diversity/richness compared to those under the control conditions. In addition, community profiling determined the prevalence of the deterministic processes overwhelming the ecological stochasticity. Analysis of both community composition and phenotypes in the TCS-exposed communities suggested the detailed deterministic mechanism: selection of TCS degrading (Sphingopyxis) and resistant (Pseudoxanthomonas) bacterial populations. The analysis also revealed a significant reduction of core activated sludge members, Chitinophagaceae (e.g., Ferruginibacter) and Comamonadaceae (e.g., Acidovorax), potentially affecting ecosystem functions (e.g., floc formation and nutrient removal) directly associated with system performance (i.e., wastewater treatment efficiency and effluent quality). Overall, our study provides new findings that inform the mechanisms underlying the community structure and diversity of activated sludge, which not only advances the current understanding of microbial ecology in activated sludge, but also has practical implications for the design and operation of environmental bioprocesses for treatment of antimicrobial-bearing waste streams.
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Project description:Soil fungi play vital roles in natural ecosystems, however, their community distribution patterns along different environmental gradients and ecological assembly processes remain unclear. In this study, Illumina MiSeq sequencing was used to investigate the soil fungal community structures of five different forest types along an elevational gradient, and a framework based on a null model was adopted to quantify the relative contribution of deterministic and stochastic ecological assembly processes. The results showed that the majority of soil fungal OTUs were derived from Zygomycota, Basidiomycota, and Ascomycota. Soil fungal community structure differed significantly among the five sites (P < 0.01), and the fungal α-diversity decreased as elevation increased (P < 0.01). The null model showed that the relative contribution of stochastic processes (37.78-73.33%) was higher than that of deterministic processes (26.67-62.22%) within the same forest type, while that of deterministic processes (35.00-93.00%) was higher than stochastic processes (7.00-65.00%) between forest types. These results suggest that forest soil fungal diversity decreased significantly with increasing elevation, and that deterministic processes may be key factors influencing soil fungal community assemblies among forest types. The results of this study provide new insight into soil fungal distribution patterns and community assembly processes in natural forest ecosystems.
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Project description: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.
| S-EPMC6145019 | biostudies-literature
Project description:Community assembly processes generate shifts in species abundances that influence ecosystem cycling of carbon and nutrients, yet our understanding of assembly remains largely separate from ecosystem-level functioning. Here, we investigate relationships between assembly and changes in microbial metabolism across space and time in hyporheic microbial communities. We pair sampling of two habitat types (i.e., attached and planktonic) through seasonal and sub-hourly hydrologic fluctuation with null modeling and temporally explicit multivariate statistics. We demonstrate that multiple selective pressures-imposed by sediment and porewater physicochemistry-integrate to generate changes in microbial community composition at distinct timescales among habitat types. These changes in composition are reflective of contrasting associations of Betaproteobacteria and Thaumarchaeota with ecological selection and with seasonal changes in microbial metabolism. We present a conceptual model based on our results in which metabolism increases when oscillating selective pressures oppose temporally stable selective pressures. Our conceptual model is pertinent to both macrobial and microbial systems experiencing multiple selective pressures and presents an avenue for assimilating community assembly processes into predictions of ecosystem-level functioning.
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Project description:Soil microbial community is a complex blackbox that requires a multi-conceptual approach (Hultman et al., 2015; Bastida et al., 2016). Most methods focus on evaluating total microbial community and fail to determine its active fraction (Blagodatskaya & Kuzyakov 2013). This issue has ecological consequences since the behavior of the active community is more important (or even essential) and can be different to that of the total community. The sensitivity of the active microbial community can be considered as a biological mechanism that regulates the functional responses of soil against direct (i.e. forest management) and indirect (i.e. climate change) human-induced alterations. Indeed, it has been highglihted that the diversity of the active community (analyzed by metaproteomics) is more connected to soil functionality than the that of the total community (analyzed by 16S rRNA gene and ITS sequencing) (Bastida et al., 2016). Recently, the increasing application of soil metaproteomics is providing unprecedented, in-depth characterisation of the composition and functionality of active microbial communities and overall, allowing deeper insights into terrestrial microbial ecology (Chourey et al., 2012; Bastida et al., 2015, 2016; Keiblinger et al., 2016). Here, we predict the responsiveness of the soil microbial community to forest management in a climate change scenario. Particularly, we aim: i) to evaluate the impacts of 6-years of induced drought on the diversity, biomass and activity of the microbial community in a semiarid forest ecocosystem; and ii) to discriminate if forest management (thinning) influences the resistance of the microbial community against induced drought. Furthermore, we aim to ascertain if the functional diversity of each phylum is a trait that can be used to predict changes in microbial abundance and ecosystem functioning.
2021-02-02 | PXD005447 | Pride
Project description:The experiment at three long-term agricultural experimental stations (namely the N, M and S sites) across northeast to southeast China was setup and operated by the Institute of Soil Science, Chinese Academy of Sciences. This experiment belongs to an integrated project (The Soil Reciprocal Transplant Experiment, SRTE) which serves as a platform for a number of studies evaluating climate and cropping effects on soil microbial diversity and its agro-ecosystem functioning. Soil transplant serves as a proxy to simulate climate change in realistic climate regimes. Here, we assessed the effects of soil type, soil transplant and landuse changes on soil microbial communities, which are key drivers in Earth’s biogeochemical cycles.
2016-02-04 | GSE77546 | GEO
Project description:It has been reported that the invasion of Spartina alterniflora changed the soil microbial community in the mangrove ecosystem in China, especially the bacterial community, although the response of soil fungal communities and soil microbial ecological functions to the invasion of Spartina alterniflora remains unclear. In this study, we selected three different communities (i.e., Spartina alterniflora community (SC), Spartina alterniflora-mangrove mixed community (TC), and mangrove community (MC)) in the Zhangjiangkou Mangrove Nature Reserve in China. High-throughput sequencing technology was used to analyze the impact of Spartina alterniflora invasion on mangrove soil microbial communities. Our results indicate that the invasion of Spartina alterniflora does not cause significant changes in microbial diversity, but it can alter the community structure of soil bacteria. The results of the LEfSe (LDA Effect Size) analysis show that the relative abundance of some bacterial taxa is not significantly different between the MC and SC communities, but different changes have occurred during the invasion process (i.e., TC community). Different from the results of the bacterial community, the invasion of Spartina alterniflora only cause a significant increase in few fungal taxa during the invasion process, and these taxa are at some lower levels (such as family, genus, and species) and classified into the phylum Ascomycota. Although the invasion of Spartina alterniflora changes the taxa with certain ecological functions, it may not change the potential ecological functions of soil microorganisms (i.e., the potential metabolic pathways of bacteria, nutritional patterns, and fungal associations). In general, the invasion of Spartina alterniflora changes the community structure of soil microorganisms, but it may not affect the potential ecological functions of soil microorganisms.
| S-EPMC7827921 | biostudies-literature