Elsevier

Applied Soil Ecology

Volume 120, November 2017, Pages 44-54
Applied Soil Ecology

Early successional patterns of bacterial communities in soil microcosms reveal changes in bacterial community composition and network architecture, depending on the successional condition

https://doi.org/10.1016/j.apsoil.2017.07.015Get rights and content

Highlights

  • Soil bacterial communities rapidly change upon abiotic or biotic perturbations.

  • Pesticide amendment increase cooperation, eukaryote inhibition increases antagonism.

  • Stochastic turnover precedes structural convergence in early successional stages.

  • Perturbation type has no effect in keystone soil bacteria persistence.

  • Presence of eukaryotes or pesticide modulates bacterial keystones interaction.

Abstract

Soil ecosystem dynamics are influenced by the composition of bacterial communities and environmental conditions. A common approach to study bacterial successional dynamics is to survey the trajectories and patterns that follow bacterial community assemblages; however early successional stages have received little attention. To elucidate how soil type and chemical amendments influence both the trajectories that follow early compositional changes and the architecture of the community bacterial networks in soil bacterial succession, a time series experiment of soil microcosm experiments was performed. Soil bacterial communities were initially perturbed by dilution and subsequently subjected to three amendments: application of the pesticide 2,4-dichlorophenoxyacetic acid, as a pesticide-amended succession; application of cycloheximide, an inhibitor affecting primarily eukaryotic microorganisms, as a eukaryotic-inhibition bacterial succession; or application of sterile water as a non-perturbed control. Terminal restriction fragment length polymorphism (T-RFLP) analysis of the 16S rRNA gene isolated from soil microcosms was used to generate bacterial relative abundance datasets. Bray-Curtis similarity and beta diversity partition-based methods were applied to identify the trajectories that follow changes in bacterial community composition. Results demonstrated that bacterial communities exposed to these three conditions rapidly differentiated from the starting point (less than 12 h), followed different compositional change trajectories depending on the treatment, and quickly converged to a state similar to the initial community (48–72 h). Network inference analysis was applied using a generalized Lotka-Volterra model to provide an overview of bacterial OTU interactions and to follow the changes in bacterial community networks. This analysis revealed that antagonistic interactions increased when eukaryotes were inhibited, whereas cooperative interactions increased under pesticide influence. Moreover, central OTUs from soil bacterial community networks were also persistent OTUs, thus confirming the existence of a core bacterial community and that these same OTUs could plastically interact according to the perturbation type to quickly stabilize bacterial communities undergoing succession.

Introduction

Biological succession (BS), or more inclusively community dynamics, may be understood as the changes in the composition or architecture of a community assemblage at a specified location over time (Pickett and McDonnell, 1989, Meiners et al., 2015). Although BS theory has been mostly applied to study macro-organism communities, several microbiological studies have used the BS concept to explain the changes that occur in microbial communities over time (Schmidt et al., 2014, Zhou et al., 2014, Brannen-Donnelly and Engel, 2015) and over time and space (Bajerski and Wagner, 2013, Storey et al., 2015, Beam et al., 2016). However, few studies have described the changes in bacterial assemblages during early stages of bacterial succession, as in colonization of pristine soil or heavily perturbed substrates.

Perturbations are incredibly varied, as are their impacts on communities (Armesto and Pickett, 1985). As such, initiating factors may represent the dominant shaping force between perturbation and succession (Meiners et al., 2015). To measure perturbation effects, changes in community diversity and composition are usually analyzed (Shade et al., 2012, Itoh et al., 2014, Brannen-Donnelly and Engel, 2015). However, little is known about how perturbations affect the early compositional trajectories and community network architecture that a perturbation-induced succession produces. Conceptual models of successional dynamics of microbial communities assume that predictable changes in species composition occur during microbial BS (Nemergut et al., 2007, Redford and Fierer, 2009, Shade et al., 2012, Fukami, 2015). Species turnover processes, i.e. the replacement of species, has been shown to occur in a predictably manner in a variety of ecosystems (Schmidt et al., 2007, Redford and Fierer, 2009, Fierer et al., 2010). Nestedness, a measure that quantifies the overlap in species composition between high- and low-diversity times (Atmar and Patterson, 1993), is especially high in fragmented ecosystems (Ruhí et al., 2013) or during late BS stages of biofilm development (Jackson et al., 2001).

Advances in techniques to estimate changes in community composition have elucidated the processes of resistance and resilience to environmental disturbances. However, understanding changes in both community composition and the interactions between organisms in that community is necessary obtain sensitive indicators of the health status of an ecosystem (Burkhard et al., 2008, Fukami, 2015). While there are extensive data on interactions between populations in communities of macro-organisms, much less is known about the interactions between bacteria, mainly because these interactions are more difficult to observe and document. Advances in bioinformatics and statistics have provided a wealth of tools for the inference of community networks; however, these methods have not yet been applied to estimate the restoration potential of an ecosystem based on changes in microbial community networks. One of the methods used for network inference is based on the Lotka-Volterra (LV) model. Using this model, it is possible to hypothesize putative interactions though inference of both the sense of the interaction (who affects whom) and the type of interaction (positive or negative) between relevant actors in the community (Faust and Raes, 2012, Berry and Widder, 2014, Agler et al., 2016, van der Heijden and Hartmann, 2016).

Considering the importance of colonizing microorganisms in initiating succession, the bacterial community composition at the beginning of succession may play a central role in shaping soil ecosystem dynamics in response to perturbations, modulating land recovery, maintaining soil health (Berendsen et al., 2012, Chaparro et al., 2012, Itoh et al., 2014, Creamer et al., 2016), and promoting successful settlement and growth of plants (Chabrerie et al., 2003, Knelman et al., 2012). Studying the composition of soil bacterial communities and successional dynamics contributes not only to the understanding of bacterial community processes occurring in soil microbial ecosystems, but also to the development of strategies for sustainable land management, as an agro-resource.

This work describes how the successional conditions of soil bacterial communities affect the trajectories of compositional changes and how these changes are reflected in the structure of community bacterial networks. Using soil microcosms prepared with a non-irradiated/irradiated (1:19) soil mix, thus imitating colonization of a resource rich substrate, three soil successional conditions were tested: no perturbation, eukaryote-inhibition, and pesticide-amendment. The non-perturbed soil microcosms (i.e. controls) received only sterile water; the eukaryote-inhibition condition was amended with cycloheximide (CHX), a compound that strongly inhibits eukaryote development, by protein synthesis inhibition (Kota et al., 1999, Manzano et al., 2007, Holmes et al., 2014); and the pesticide condition was amended with the chloroaromatic pollutant herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), whose toxic effects are well-described (Kraiser et al., 2013). The choice of the compounds used (CHX and 2,4-D) obeys to the characteristics of both compounds. CHX can kill or inhibit a significant fraction of soil eukaryotes, diminishing the activity of bacterial predators and affecting the interaction between fungi and bacteria. However, the most remarkable use of CHX is as an antiprotozoal chemical. CHX has been used as a eukaryotic growth inhibitor both for bacterial isolation (Capozzi et al., 2012) and to test ecological hypotheses in bacterial predation experiments (Kota et al., 1999, Holmes et al., 2014). The pesticide 2,4-D, which has been used for decades for weed control, was chosen as an example of anthropic perturbation. This decision is supported by a wealth of studies available on the effects of 2,4-D on soil and particularly on microbial communities (Pérez-Pantoja et al., 2003, Vroumsia et al., 2005, Manzano et al., 2007, Gazitúa et al., 2010). Together, these treatments represent examples of disturbances produced by pesticides or by inhibition of eukaryotes.

Using varied statistical and computational data analysis strategies, we aimed to test the hypothesis that soil successional conditions determine both the trajectories that follow early compositional changes and the architecture of bacterial community networks, adding to our understanding of the ecological rules that dictate community stability.

Section snippets

Soil samples and microcosm design

Soil microcosms were prepared with a grassland soil from San Borja Park (SB) (33° 26′ S, 70° 39′ W) or with an agricultural soil from Calera de Tango (CT) (33° 36′ S; 70° 45′ W), both located near Santiago, Chile. Soil samples were taken to the laboratory, sieved through a 2 mm mesh and dried at 40 °C for 48 h. The homogenized material was subjected to physical and chemical characterization (Supplementary material, Table S1), using standard procedures (Sparks et al., 1996). One kilogram of each

Comparing early successional dynamics in two soils

To study early successional dynamics of bacterial communities, the 16S rRNA gene T-RFLP profiles from agricultural (CT) and grassland (SB) soil microcosms were compared. NMDS analysis of these T-RFLP profiles showed a clear grouping of samples according to soil type for all sampling times analyzed (Fig. 1). ANOSIM comparisons between soil type groups indicated that the observed groupings were statistically significant with a global R-value of 1 (p = 0.001). The R-value of 1 indicated that the

Discussion

Bacterial community structures in soil microcosms exposed to different conditions were compared through the culture independent molecular technique T-RFLP (Hartmann and Widmer, 2008, Schütte et al., 2008) and with a combination of multivariate and network based analyses. The results reported here indicate that soil bacterial communities exposed to a resource-rich substrate, as a result of extensive irradiation (i.e. almost sterile conditions) increased organic matter availability, and altered

Acknowledgements

Work funded by CONICYT to the Centre of Applied Ecology and Sustainability, grant FB 0002-2014.

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