Disentangling temporal associations in marine microbial networks

Abstract

Microbial interactions are fundamental for Earth’s ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. The omics-based censuses are helpful to predict microbial interactions through the inference of static association networks. However, since microbial interactions are highly dynamic, we have developed an approach to generate a temporal network from a single static network. We applied it to understand the monthly microbial associations’ dynamics occurring over ten years in the Blanes Bay Microbial Observatory (Mediterranean Sea). For the decade, we identified persistent, seasonal, and temporary microbial associations. Moreover, we found that the temporal network appears to follow an annual cycle, collapsing and reassembling when transiting between colder and warmer waters. We observed higher repeatability in colder than warmer months. Altogether, our results indicate that marine microbial networks follow recurrent temporal dynamics, which need to be accounted to better understand the dynamics of the ocean microbiome.

Oceanic heterotrophic flagellates are dominated by a few widespread taxa

“…We did not aim to use co‐occurrence as a direct proxy of interaction (Blanchet et al 2020), but to identify potentially interesting biotic relationships that would then need to be experimentally validated (Carr et al 2019). Even after removing associations that could arise from shared environmental preferences (Röttjers and Faust 2018; Deutschmann et al 2021), HF and prokaryotic ASVs were placed in two differentiated clusters formed by abundant taxa with different thermal preferences, a phenomenon already seen in previous studies (Pommier et al 2007; Fuhrman et al 2008; Lima‐Mendez et al 2015). At a broad scale, specific HF did not show preferential correlations with specific prokaryotic taxa, and this agrees with the perception that cell size is the main factor in prey vulnerability (Jürgens and Massana 2008).…”

“…We carried out co‐occurrence network analyses using sparCC (Friedman and Alm 2012) as implemented in fastSpar v0.0.7 (Watts et al 2019). We used EnDED (Deutschmann et al 2019) as implemented in Deutschmann et al (2021) on the obtained correlation matrix to remove associations driven by environmental variables (temperature, conductivity, dissolved oxygen, and fluorescence). We carried out network processing with tidygraph v1.2.0 (Pedersen 2020 a ) and ggraph v2.0.5 (Pedersen 2020 b ).…”

Disentangling microbial networks across pelagic zones in the global ocean

“…Core microorganisms are often defined as those appearing in most or all samples from similar habitats (Shade & Handelsman, 2012). We previously identified a core microbiota in a coastal MS observatory based on both association patterns (Krabberød et al, 2021) and temporal recurrence of associations (Deutschmann et al, 2021). Both studies indicate more robust microbial connectivity, suggesting a broader core, in colder than in warmer seasons.…”

“…FlashWeave could detect indirect edges and allows to supply additional metadata such as environmental variables, but currently does not support missing data. Thus, we applied EnDED (Deutschmann et al 2020), combining the methods Interaction Information (with 0.05 significance threshold and 10000 iterations) and Data Processing Inequality as done previously via artificially-inserted edges to connect all microbial nodes to the six environmental parameters (Deutschmann et al, 2021). Although EnDED can handle missing environmental data when calculating intermediate values relating ASV and environmental factors, it would compute intermediate values for microbial edges using all samples.…”

“…Regional networks, however, require a high number of samples per delineated zone, but these may not be available due to logistic or budgetary limitations. Recent approaches circumvent this limitation by deriving sample-specific subnetworks from a single static, i.e., all-sample network, which allows quantifying association recurrence over spatiotemporal scales (Chaffron et al, 2020;Deutschmann et al, 2021). Here, we adjusted this approach and used it to determine global and regional associations along vertical and horizontal ocean scales, which allowed us determining the biogeography of marine microbial associations.…”