Here we describe, the longest microbial time-series analyzed to date using high-resolution 16S rRNA tag pyrosequencing of samples taken monthly over 6 years at a temperate marine coastal site off Plymouth, UK. Data treatment effected the estimation of community richness over a 6-year period, whereby 8794 operational taxonomic units (OTUs) were identified using single-linkage preclustering and 21 130 OTUs were identified by denoising the data. The Alphaproteobacteria were the most abundant Class, and the most frequently recorded OTUs were members of the Rickettsiales (SAR 11) and Rhodobacteriales. This near-surface ocean bacterial community showed strong repeatable seasonal patterns, which were defined by winter peaks in diversity across all years. Environmental variables explained far more variation in seasonally predictable bacteria than did data on protists or metazoan biomass. Change in day length alone explains >65% of the variance in community diversity. The results suggested that seasonal changes in environmental variables are more important than trophic interactions. Interestingly, microbial association network analysis showed that correlations in abundance were stronger within bacterial taxa rather than between bacteria and eukaryotes, or between bacteria and environmental variables.

For two centuries, biologists have documented a gradient of animal and plant biodiversity from the tropics to the poles but have been unable to agree whether it is controlled primarily by productivity, temperature, or historical factors. Recent reports that find latitudinal diversity gradients to be reduced or absent in some unicellular organisms and attribute this to their high abundance and dispersal capabilities would suggest that bacteria, the smallest and most abundant organisms, should exhibit no latitudinal pattern of diversity. We used amplified ribosomal intergenic spacer analysis (ARISA) whole-assemblage genetic fingerprinting to quantify species richness in 103 near-surface samples of marine bacterial plankton, taken from tropical to polar in both hemispheres. We found a significant latitudinal gradient in richness. The data can help to evaluate hypotheses about the cause of the gradient. The correlations of richness with latitude and temperature were similarly strong, whereas correlations with parameters relating to productivity (chlorophyll, annual primary productivity, bacterial abundance) and other variables (salinity and distance to shore) were much weaker. Despite the high abundance and potentially high dispersal of bacteria, they exhibit geographic patterns of species diversity that are similar to those seen in other organisms. The latitudinal gradient in marine bacteria supports the hypothesis that the kinetics of metabolism, setting the pace for life, has strong influence on diversity.

Microbes have central roles in ocean food webs and global biogeochemical processes, yet specific ecological relationships among these taxa are largely unknown. This is in part due to the dilute, microscopic nature of the planktonic microbial community, which prevents direct observation of their interactions. Here, we use a holistic (that is, microbial system-wide) approach to investigate time-dependent variations among taxa from all three domains of life in a marine microbial community. We investigated the community composition of bacteria, archaea and protists through cultivation-independent methods, along with total bacterial and viral abundance, and physico-chemical observations. Samples and observations were collected monthly over 3 years at a well-described ocean time-series site of southern California. To find associations among these organisms, we calculated time-dependent rank correlations (that is, local similarity correlations) among relative abundances of bacteria, archaea, protists, total abundance of bacteria and viruses and physico-chemical parameters. We used a network generated from these statistical correlations to visualize and identify time-dependent associations among ecologically important taxa, for example, the SAR11 cluster, stramenopiles, alveolates, cyanobacteria and ammonia-oxidizing archaea. Negative correlations, perhaps suggesting competition or predation, were also common. The analysis revealed a progression of microbial communities through time, and also a group of unknown eukaryotes that were highly correlated with dinoflagellates, indicating possible symbioses or parasitism. Possible ‘keystone’ species were evident. The network has statistical features similar to previously described ecological networks, and in network parlance has non-random, small world properties (that is, highly interconnected nodes). This approach provides new insights into the natural history of microbes.

Factors influencing patterns in the distribution and abundance of plant and animal taxa modulate ecosystem function and ecosystem response to environmental change, which is often taken to infer low functional redundancy among such species, but such relationships are poorly known for microbial communities. Using high-resolution molecular fingerprinting, we demonstrate the existence of extraordinarily repeatable temporal patterns in the community composition of 171 operational taxonomic units of marine bacterioplankton over 4.5 years at our Microbial Observatory site, 20 km off the southern California coast. These patterns in distribution and abundance of microbial taxa were highly predictable and significantly influenced by a broad range of both abiotic and biotic factors. These findings provide statistically robust demonstration of temporal patterning in marine bacterial distribution and abundance, which suggests that the distribution and abundance of bacterial taxa may modulate ecosystem function and response and that a significant subset of the bacteria exhibit low levels of functional redundancy as documented for many plant and animal communities.

Motivation: Characterizing the diversity of microbial communities and understanding the environmental factors that influence community diversity are central tenets of microbial ecology. The development and application of cultivation independent molecular tools has allowed for rapid surveying of microbial community composition at unprecedented resolutions and frequencies. There is a growing need to discern robust patterns and relationships within these datasets which provide insight into microbial ecology. Pearson correlation coefficient (PCC) analysis is commonly used for identifying the linear relationship between two species, or species and environmental factors. However, this approach may not be able to capture more complex interactions which occur in situ; thus, alternative analyses were explored. Results: In this paper we introduced local similarity analysis (LSA), which is a technique that can identify more complex dependence associations among species as well as associations between species and environmental factors without requiring significant data reduction. To illustrate its capability of identifying relationships that may not otherwise be identified by PCC, we first applied LSA to simulated data. We then applied LSA to a marine microbial observatory dataset and identified unique, significant associations that were not detected by PCC analysis. LSA results, combined with results from PCC analysis were used to construct a theoretical ecological network which allows for easy visualization of the most significant associations. Biological implications of the significant associations detected by LSA were discussed. We also identified additional applications where LSA would be beneficial. Availability: The algorithms are implemented in Splus/R and they are available upon request from the corresponding author. Contact:fsun@usc.edu