Bacterial communities in oil contaminated soils: Biogeography and co-occurrence patterns

Abstract

To improve the knowledge about the biogeographic patterns of bacteria in soils contaminated with crude oil, we studied the effects of local geochemical properties and geographic distance on bacterial community structure in oil contaminated soil in five oil refineries (46 to 360 km apart). The microbial structure was significantly affected by soil environmental factors such as pH, total petroleum hydrocarbons (TPH), total nitrogen, and cadmium level. Microbial alpha-diversity was positively correlated with pH but negatively correlated with TPH. Among sampling sites, the community dissimilarities increased with spatial distance. Variation of bacterial community was mostly attributed to simultaneous effects of spatial distance and environmental factors, and purely spatial distance contributed more to the variation. Microbial generalist OTUs were broadly distributed and dominant in contaminated soils. Their populations were in low proportion (15.75%), but they had high relative abundance (65.05%), and some were associated with TPH-degradation. Network analysis indicated that microbial communities had non-random co-occurrence patterns. Keystone taxa were Rubrivivax, Nitrospira, Methylotenera, Methyloversatilis and Acidaminobacter. Microbial taxa from the same module had strong ecological linkages and were involved in biological electron-transfer, C and N-cycles, and organic contaminant degradation. Our results indicate that the same microbial groups with TPH-degradation ability can be assembled from indigenous microorganisms in separate regions through long term exposure to contamination.

Hydrocarbon degraders establish at the costs of microbial richness, abundance and keystone taxa after crude oil contamination in permafrost environments

“…However, to advance further from the basic inventory description of the composition and diversity of microbial communities, new analytical tools that enable to infer also on biogeographical patterns, potential interactions between taxa, shared ecological niches, keystone microbial groups, network and non-random co-occurrence patterns amongst taxa should be prompted16. Network-based data analysis has recently been applied to large scale environmental sequencing in a few studies to gain insights, for examples, into microbial interactions in soil1718 including upon contamination with crude oil1920.…”

Fungal Communities in Ancient Peatlands at Sanjiang Plain, China

“…Differences in soil properties across samples were determined using ANOVA followed by Least Significant Difference (LSD) test in IBM SPSS (version 19.0, Chicago, IL, USA) [40]. The beta-diversity community was compared by permutational MANOVA [41]. The Illumina sequencing data in the present study has been deposited into NCBI SRA database with the accession number as No.…”

Temporal dynamics of soil bacterial communities and multifunctionality are more sensitive to introduced plants than to microbial additions in a multicontaminated soil

“…Microorganisms exhibit remarkable stability in the face of disturbances, largely due to their high metabolic flexibility and physiological tolerance, as well as high abundance, widespread dispersal, rapid growth, and evolutionary adaptation (Allison & Martiny, ; Fuhrman, Cram, & Needham, ). Generally, the stability of microbial ecosystems is determined by three mechanisms (Allison & Martiny, ): (a) resistance, when the microbial community displays tolerance to a disturbance (Jiao et al, ; Jiao et al, ); (b) resilience, when the microbial community is changed by a disturbance yet rapidly recovers to its initial or alternative stable state (Griffiths & Philippot, ; Hodgson, McDonald, & Hosken, ); and (c) functional redundancy, when after a disturbance the ecosystem processes remain similar to their original state, despite the microbial community being substantially altered without recovery. Meanwhile, an individual microbial species can adopt three primary response strategies to environmental disturbances according to their apparent adaptations: (a) adapt and maintain the abundance unchanged (i.e., ‘tolerant’); (b) become negatively affected and reduced in abundance (i.e., ‘sensitive’); and (c) benefit from the new conditions and increase in abundance (i.e., ‘opportunist’; Evans & Hofmann, ; Shade et al, ; Szekely & Langenheder, ).…”

“…Environmental contamination is a global problem that causes damage to natural ecosystems and harms animal health (Jiao, Liu, et al, ). Due to wastewater irrigation or illegal discharges, an increasing influx of inorganic (e.g., heavy metals) and organic (e.g., petroleum hydrocarbons) contaminants could change soil physicochemical properties, leading to serious land degradation (Bayat et al, ; Dawson et al, ; Peterson et al, ; Wang, Zhao, Zeng, Hu, & Yu, ).…”

“…Moreover, historical contingency, known as priority effects, is typically an obstacle for soil colonization by newly introduced microbial species (Vannette & Fukami, ). Currently, there are a growing number of studies investigating the temporal dynamics of microbial communities in response to environmental contamination, defined as microbial succession (Fierer, Nemergut, Knight, & Craine, ; Jiao, Chen, et al, ; Jiao, Liu, et al, ). Yet we still lack basic knowledge of how microbial species interact and idiosyncratic effects influence biodiversity and ecosystem functioning during microbial community succession with one or more introduced microbial consortia (Calderon et al, ; Harris, ; Laughlin, ).…”