2019
DOI: 10.1128/mbio.01607-19
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Linking Microbial Community Structure to Trait Distributions and Functions Using Salinity as an Environmental Filter

Abstract: The structure and function of microbial communities vary along environmental gradients; however, interlinking the two has been challenging. In this study, salinity was used as an environmental filter to study how it could shape trait distributions, community structures, and the resulting functions of soil microbes. The environmental filter was applied by salinizing nonsaline soil (0 to 22 mg NaCl g−1). Our targeted community trait distribution (salt tolerance) was determined with dose-response relationships be… Show more

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Cited by 50 publications
(34 citation statements)
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References 59 publications
(83 reference statements)
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“…Salinity has been reported to selectively affect certain phylogenetic groups by markedly reducing cell-specific activities and growth efficiency (Del Giorgio & Bouvier, 2002). Simultaneously, microbes physiologically adapt to offset the adverse effects of salinity (Rath, Maheshwari, Rousk, & Bailey, 2019). Therefore, the changing salinity could act as an environmental filter for bacterial community, possibly leading to the 'loss' or the 'activation' of some species (Rath et al, 2019).…”
mentioning
confidence: 99%
See 1 more Smart Citation
“…Salinity has been reported to selectively affect certain phylogenetic groups by markedly reducing cell-specific activities and growth efficiency (Del Giorgio & Bouvier, 2002). Simultaneously, microbes physiologically adapt to offset the adverse effects of salinity (Rath, Maheshwari, Rousk, & Bailey, 2019). Therefore, the changing salinity could act as an environmental filter for bacterial community, possibly leading to the 'loss' or the 'activation' of some species (Rath et al, 2019).…”
mentioning
confidence: 99%
“…Simultaneously, microbes physiologically adapt to offset the adverse effects of salinity (Rath, Maheshwari, Rousk, & Bailey, 2019). Therefore, the changing salinity could act as an environmental filter for bacterial community, possibly leading to the 'loss' or the 'activation' of some species (Rath et al, 2019). Salinity has been confirmed to directly affect the bacterial community in different ways.…”
mentioning
confidence: 99%
“…Another potential cause for the increased rate similarity could be that in the long term the fluxes in this A B C D poorly mixed system are strongly constrained by boundary conditions and physical transport properties (Louca et al, 2019). A negative effect of salinity on metabolic rates has been observed previously, especially when microorganisms were originally taken from a less saline environment (as in our case) (Wilson et al, 2013;Rath and Rousk, 2015;Peng et al, 2017;Navada et al, 2019;Rath et al, 2019). The effects of salinity are at least partly physiological in nature, with salinity imposing a stress to cells (Rath and Rousk, 2015) that might lead to lower microbial population sizes and generally lower bulk metabolic rates.…”
Section: Metabolic Activitymentioning
confidence: 51%
“…Experiments with microcosms have been particularly useful for disentangling the relationships between environmental conditions, taxonomy, metabolic composition and metabolic function in microbial systems (Widder et al, 2016). Many experiments use chemical treatments to modify microbial taxonomic composition in microcosms, and then examine the effects on a given function (Girvan et al, 2005;Langenheder et al, 2005Langenheder et al, , 2006Peter et al, 2011;Harter et al, 2014;Peng et al, 2017;Rath et al, 2019). Common garden experiments, where microbial communities of different origins are subjected to identical conditions, have also been used to examine the potential effects of origin on metabolic rates (Strickland et al, 2009;Reed and Martiny, 2013;Martiny et al, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…Increasing evidence shows that microbial community composition changes along environmental gradients [12,13] and from healthy to diseased states [4,14]. Most existing approaches can only infer a single aggregate network from a number of samples, failing to characterize how microbial interactions vary according to geographic, biotic, and developmental contexts and further chart causal links between these interactions and biogeochemical cycling or host phenotypes.…”
Section: Introductionmentioning
confidence: 99%