Microbiomes in light of traits: A phylogenetic perspective

By J. Martiny, Stuart E. Jones, J. Lennon, Adam C. Martiny. Science, 2015

741 citations recorded by the source corpus.

Subfields: Ecology, Food Science, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/703c7d4cab8a9c2f8e65bb33ea48d565dd60c567

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Citations in the corpus

Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils (2020, background): “However, temperature adaptation is conserved in the bacterial phylogeny only at a shallow level (Martiny et al., 2015).”
Phage Therapy in the Postantibiotic Era (2019, background): “Current research suggests that phage resistance is a nonphylogenetically conserved, highly species-specific, evolutionary trait (142).”
Stochastic Community Assembly: Does It Matter in Microbial Ecology? (2017, background): “Fortunately, a recent review showed that microbial traits appear to be phylogenetically conserved (268), and there were strong phylogenetic signals for many microbial traits (264).”
The healthy human microbiome (2016, background): “This suggests that one definition of a “core” healthy microbiome might include specific microbial gene family combinations, metabolic modules, and regulatory pathways that together promote a stable host-associated ecology [96, 97].”
A trait-based approach to ocean ecology (2018, methodology): “Trait-based approaches have also been adopted to describe and model marine phytoplankton communities (Bruggeman and Kooijman, 2007; Follows et al., 2007; Monteiro et al., 2010; Follows and Dutkiewicz, 2011; Edwards et al., 2013; Martiny et al., 2015), protistan plankton systems (Chakraborty et al.”
Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system (2016, background): “The ﬁndings relating to metabolic network topology will guide future in silico studies of inter-organism metabolic networks 50 , and may have application in trait-based ecosystem models that are needed to predict the impacts of changing environmental conditions on biogeochemical cycles 51 .”