Annually reoccurring bacterial communities are predictable from ocean conditions

By Jed A. Fuhrman, I. Hewson, Michael S. Schwalbach, Joshua A. Steele, Mark V Brown, Shahid Naeem. Proceedings of the National Academy of Sciences of the United States of America, 2006

611 citations recorded by the source corpus.

Subfields: Ecology, Oceanography

View Paper: https://www.semanticscholar.org/paper/8d9a339a40e3d7c5e5eb4239c13b1e1c7935a8a2

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Citations in the corpus

A latitudinal diversity gradient in planktonic marine bacteria (2008, background): “The different bacterial taxa defined by ARISA ‘‘behave’’ like different species; OTUs identified by ARISA at our San Pedro Channel study site are seasonally variable, annually repeatable, and highly predictable from environmental parameters, with different OTUs associated with different parameters (”
Linking metagenomics to aquatic microbial ecology and biogeochemical cycles (2019, neutral): “Studies based on SSU rRNA gene sequencing approaches often include a modeling component, often in the form of statistical approaches that correlate the relative abundance of individual taxa to each other or to abiotic environmental parameters (Fuhrman et al. 2006; Gilbert et al. 2011).”
The rare bacterial biosphere. (2012, background): “For example, the same dominant populations have been found year after year on the Pacific coast (Fuhrman et al. 2006).”
Inter‐annual recurrence of archaeal assemblages in the coastal NW Mediterranean Sea (Blanes Bay Microbial Observatory) (2010, background): “Such marine microbial observatories have demonstrated reoccurring and predictable patterns in bacterial communities (Fuhrman et al. 2006) and have unveiled bacterial seasonal dynamics (Alonso-Sáez et al. 2007).”
Multivariate analyses in microbial ecology (2007, methodology): “For instance, Fuhrman et al. (2006) used the technique to evidence the existence of repeatable temporal patterns in the community composition of marine bacterioplankton over 4.5 years.”
Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean (2013, background): “…a combination of bottom-up and top-down mechanisms will select for different subsets of microbial diversity over the course of the year, causing predictable patterns of community turnover (Fuhrman et al. 2006, Carlson et al. 2009, Treusch et al. 2009, Caporaso et al. 2012, Gilbert et al. 2012).”
Fundamentals of Microbial Community Resistance and Resilience (2012, background): “Also, seasonal trajectories are common in freshwater, marine, and sediment systems (Fuhrman et al., 2006; Christian and Lind, 2007; Shade et al., 2007; Nelson, 2008; Andersson et al., 2009; Crump et al., 2009; Gilbert et al., 2009, 2010).”
Dormancy contributes to the maintenance of microbial diversity (2010, background): “Dormancy has the potential to explain numerous ecological patterns observed in microbial systems over the past decade, including repeated seasonal succession (6), the rare microbial biosphere (22), and the apparent resilience to disturbance of microbially mediated processes (48).”
Stochastic and deterministic assembly processes in subsurface microbial communities (2012, background): “Higher levels of temporal environmental variation may therefore deterministically drive community dynamics, consistent with recent studies linking cyclical environmental conditions to cyclical patterns in microbial community composition (Fuhrman et al., 2006; Gilbert et al., 2012).”
Global phylogenetic community structure and β-diversity patterns in surface bacterioplankton metacommunities (2010, background): “…superficial waters, other factors could have escaped our analysis, such as the temporal component that may lead to some predictable patterns (Fuhrman et al. 2006) or the influence of biotic factors on bacterial community composi- 8 Barberán & Casamayor: Surface bacterioplankton…”
Community structure of marine bacterioplankton: patterns, networks, and relationships to function (2008, neutral): “The monthly patterns of community composition in nearsurface (5 m water depth) samples were studied over 4 yr using a statistical technique called discriminant function analysis (DFA), linked with autocorrelation and multiple regression (Fuhrman et al. 2006).”