Bacterioplankton community resilience to ocean acidification: evidence from microbial network analysis

Abstract

Ocean acidification (OA), caused by seawater CO2 uptake, has significant impacts on marine calcifying organisms and phototrophs. However, the response of bacterial communities, who play a crucial role in marine biogeochemical cycling, to OA is still not well understood. Previous studies have shown that the diversity and structure of microbial communities change undeterminably with elevated pCO2. Here, novel phylogenetic molecular ecological networks (pMENs) were employed to investigate the interactions of native bacterial communities in response to OA in the Arctic Ocean through a mesocosm experiment. The pMENs results were in line with the null hypothesis that elevated pCO2/pH does not affect biogeochemistry processes. The number of nodes within the pMENs and the connectivity of the bacterial communities were similar, despite increased pCO2 concentrations. Our results indicate that elevated pCO2 did not significantly affect microbial community structure and succession in the Arctic Ocean, suggesting bacterioplankton community resilience to elevated pCO2. The competitive interactions among the native bacterioplankton, as well as the modular community structure, may contribute to this resilience. This pMENs-based investigation of the interactions among microbial community members at different pCO2 concentrations provides a new insight into our understanding of how OA affects the microbial community.

Interactive network configuration maintains bacterioplankton community structure under elevated CO<sub>2</sub> in a eutrophic coastal mesocosm experiment

“…This to some extent led to maintenance of bacterioplankton community structure under the ocean acidification stimuli in the context of eutrophic conditions. Additionally, these data indicate that more negative than positive relationships between OTUs were observed in both HC and LC treatments, which is consistent with a previous ocean acidification mesocosm study conducted in the Arctic Ocean (Wang et al, 2016). It was proposed that a community with more competitors would be more stable and yield less variation under environmental fluctuations (Gonzalez and Loreau, 2009).…”

“…Compared to the effects of the phytoplankton bloom, ocean acidification did not strongly influence the bacterioplankton community structure. The results indicate that bacterial abundance and community structure at different taxonomic levels were generally similar between the HC and LC treatments at the different diatom bloom stages, in line with many previous ocean acidification mesocosm bacterioplankton community studies (Tanaka et al, 2008;Wang et al, 2016;Zhang et al, 2013;Ray et al, 2012Ray et al, , 2013Baltar et al, 2015). Differences in bacterioplankton community diversity between the HC and LC treatments were also not remarkable.…”

“…Although effects of elevated CO 2 on bacterioplankton communities have been reported Tanaka et al, 2008;Wang et al, 2016;Zhang et al, 2013;Ray et al, 2012;Roy et al, 2013;Baltar et al, 2015;reviewed in Hutchins and Fu, 2017), how marine bacteria communities react to the occurrence of elevated CO 2 in eutrophic seawater is still uncertain. This mesocosm study comprehensively investigated the effects of elevated CO 2 on bacterioplankton community structure and networks using Illumina sequencing and ecological network analysis in the context of eutrophication.…”

“…Previous studies using ecological network analysis showed that elevated CO 2 significantly impacted soil bacterial/archaeal community networks by decreasing the connections for dominant fungal species and reassembling unrelated fungal species in a grassland ecosystem (Tu et al, 2015). It was also reported using ecological network analysis that elevated pCO 2 did not significantly affect microbial community structure and succession in the Arctic Ocean, suggesting bacterioplankton community resilience to elevated pCO 2 (Wang et al, 2016).…”

“…As previously described, ecological network construction and analyses were performed based on the relative abundance of OTUs in HC and LC treatments with three biological replicates (http://129.15.40.240/mena/; Wang et al, 2016). The sequencing data from each mesocosm bag with time series throughout the experiment were considered as different replicates.…”