Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Recent advances in studying the dynamics of marine microbial communities have shown that the composition of these communities follows predictable patterns and involves complex network interactions, which shed light on the underlying processes regulating these globally important organisms. Such 'holistic' (or organism- and system-based) studies of these communities complement popular reductionist, often culture-based, approaches for understanding organism function one gene or protein at a time. In this Review, we summarize our current understanding of marine microbial community dynamics at various scales, from hours to decades. We also explain how the data illustrate community resilience and seasonality, and reveal interactions among microorganisms.

The "transfer efficiency" of sinking organic particles through the mesopelagic zone and into the deep ocean is a critical determinant of the atmosphere−ocean partition of carbon dioxide (CO 2 ). Our ability to detect large-scale spatial variations in transfer efficiency is limited by the scarcity and uncertainties of particle flux data. Here we reconstruct deep ocean particle fluxes by diagnosing the rate of nutrient accumulation along transport pathways in a data-constrained ocean circulation model. Combined with estimates of organic matter export from the surface, these diagnosed fluxes reveal a global pattern of transfer efficiency to 1,000 m that is high (∼25%) at high latitudes and low (∼5%) in subtropical gyres, with intermediate values in the tropics. This pattern is well correlated with spatial variations in phytoplankton community structure and the export of ballast minerals, which control the size and density of sinking particles. These findings accentuate the importance of high-latitude oceans in sequestering carbon over long timescales, and highlight potential impacts on remineralization depth as phytoplankton communities respond to a warming climate.biological pump | organic particles | remineralization | transfer efficiency | ocean carbon storage S inking organic particles deliver carbon from the surface euphotic zone (upper ∼100 m) of the ocean into deeper layers that do not exchange with the atmosphere (1). The longevity of oceanic carbon storage by this "biological pump" depends on the depth at which particulate organic matter (POM) decays and releases CO 2 into seawater (2). Most POM is consumed within the mesopelagic zone (100−1,000 m) of the water column, which recirculates rapidly to the surface, leaving only a small fraction to remineralize in the deep ocean where carbon can be sequestered on centennial and longer timescales (3). The "transfer efficiency" of particulate carbon from the euphotic zone to depth is therefore a critical determinant of atmospheric pCO 2 (4), but its underlying controls are poorly understood and crudely represented in Earth system models used to project global carbon cycling and climate.The depth scale over which POM fluxes attenuate hinges on both the sinking speed of particles and their rate of decomposition (5), each governed by a range of factors. Decomposition rates are thought to depend on the abundance of heterotrophic microbes (6) and the temperature sensitivity of their metabolism (7,8), as well as the palatability of the organic matter itself (9, 10). Particle sinking speeds depend on their size and density, which may be ultimately dictated by the plankton community structure and trophic web of the euphotic zone where they are produced (11,12). Because these factors exhibit distinct regional variations, their relative importance might be discerned by detecting large-scale patterns of transfer efficiency in the ocean.Arrays of neutrally buoyant sediment traps deployed at multiple depths provide the most direct estimate of particle fluxes from the euphotic...

BackgroundThe increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval.ResultsWe extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified.ConclusionsThe extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

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).