Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight

Abstract

Time-series are critical to understanding long-term natural variability in the oceans. Bacterial communities in the euphotic zone were investigated for over a decade at the San Pedro Ocean Time-series station (SPOT) off southern California. Community composition was assessed by Automated Ribosomal Intergenic Spacer Analysis (ARISA) and coupled with measurements of oceanographic parameters for the surface ocean (0-5 m) and deep chlorophyll maximum (DCM, average depth B30 m). SAR11 and cyanobacterial ecotypes comprised typically more than one-third of the measured community; diversity within both was temporally variable, although a few operational taxonomic units (OTUs) were consistently more abundant. Persistent OTUs, mostly Alphaproteobacteria (SAR11 clade), Actinobacteria and Flavobacteria, tended to be abundant, in contrast to many rarer yet intermittent and ephemeral OTUs. Association networks revealed potential niches for key OTUs from SAR11, cyanobacteria, SAR86 and other common clades on the basis of robust correlations. Resilience was evident by the average communities drifting only slightly as years passed. Average Bray-Curtis similarity between any pair of dates was B40%, with a slight decrease over the decade and obvious near-surface seasonality; communities 8-10 years apart were slightly more different than those 1-4 years apart with the highest rate of change at 0-5 m between communities o4 years apart. The surface exhibited more pronounced seasonality than the DCM. Inter-depth Bray-Curtis similarities repeatedly decreased as the water column stratified each summer. Environmental factors were better predictors of shifts in community composition than months or elapsed time alone; yet, the best predictor was community composition at the other depth (that is, 0-5 m versus DCM).

Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes

“…We previously reported that members of the SAR11 cluster were correlated on a daily time scale but not monthly (Fuhrman and Steele, 2008;Steele et al, 2011;Needham et al, 2013;Fuhrman et al, 2015), reflecting patterns of temporal variability in network structure seen in other systems (Alarcón et al, 2008). A common feature of networks is the presence of modules, that is, highly interconnected groups of nodes (Newman, 2006;Olesen et al, 2007;Ings et al, 2009) and microbial networks are no exception Chow et al, 2013). Modules in other studies have been shown to represent groups of organisms that have similar seasonal patterns (Gilbert et al, 2012) that sometimes occur independently of environmental parameters and may represent organisms that are associated with each other through symbiosis, allelopathy, common niches or through other means Chow et al, 2013).…”

“…Microbial communities throughout the water column show long-term, seasonal and short-term dynamics that relate to various biological, chemical and physical environmental parameters (Gilbert et al, 2012;Giovannoni and Vergin, 2012;Chow et al, 2013;Hatosy et al, 2013;Needham et al, 2013;Cram et al, 2014a;Fuhrman et al, 2015). Microbial interactions have been observed experimentally (Jurgens et al, 1999;Miller and Bassler, 2001;Jürgens and Matz, 2002;Bonilla-Findji et al, 2009) through physical attachment ) and inferred through genomic and physiological information (for example, Bothe et al, 2010).…”

“…In marine surface waters, studies using pairwise association analysis over time have suggested that the abundance of particular bacteria tend to be best predicted by the abundance of other microorganisms, rather than variability of measured parameters (Fuhrman and Steele, 2008;Steele et al, 2011;Chow et al, 2013Chow et al, , 2014. Network analysis has identified many associations between marine microorganisms that are driven by seasonal variability, especially at locations where seasonality is strong (Gilbert et al, 2012), and this seasonal pattern has been de-convoluted in lake environments to show different inter-organismal associations independent of seasonality (Kara et al, 2012).…”

“…Previously, Chow et al (2013) examined similarities between association networks in the surface and deep chlorophyll maximum (DCM) at the San Pedro Ocean Time-series (SPOT) and observed some associations that were consistent between depths, specifically associations between operational taxonomic units (OTUs) from the SAR11 clade and other OTUs. Other associations differed between depths such as the relationships between temperature, salinity and nutrient concentrations to the abundances of many OTUs.…”

“…A common feature of networks is the presence of modules, that is, highly interconnected groups of nodes (Newman, 2006;Olesen et al, 2007;Ings et al, 2009) and microbial networks are no exception Chow et al, 2013). Modules in other studies have been shown to represent groups of organisms that have similar seasonal patterns (Gilbert et al, 2012) that sometimes occur independently of environmental parameters and may represent organisms that are associated with each other through symbiosis, allelopathy, common niches or through other means Chow et al, 2013). Here, we extend these prior network analyses of euphotic zone depths to the rest of the water column.…”