Warmer temperatures favor slower-growing bacteria in natural marine communities

Abreu, Clare I.; Bello, Martina Dal; Bunse, Carina; Pinhassi, Jarone; Gore, Jeff

doi:10.1126/sciadv.ade8352

Cited by 19 publications

(6 citation statements)

References 84 publications

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“…S15). The model captures the latitudinal transition from oligotrophs to copiotrophs in the surface, which is consistent with previous theory and modeling of both phytoplankton and picoheterotrophs 20,[33][34][35] (Fig. 2H and I).…”

Section: Modeling Picoheterotrophic Functional Diversitysupporting

confidence: 90%

Predictable functional biogeography of marine microbial heterotrophs

Zakem,

McNichol,

Weissman

et al. 2024

Preprint

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Microbial heterotrophs (`picoheterotrophs') drive global carbon cycling, but how to quantitatively organize their functional complexity remains unclear. Here, we generate a global-scale, mechanistic understanding of marine picoheterotrophic functional biogeography with a novel model-data synthesis. We build picoheterotrophic diversity into a trait-based marine ecosystem model along two axes: substrate lability and optimization for growth rate (copiotrophy) vs. substrate affinity (oligotrophy). Using genetic sequences along an Alaska-to-Antarctica Pacific Ocean transect, we compile 21 picoheterotrophic guilds and estimate their degree of copiotrophy. Data and model agreement suggests that gradients in predation and substrate lability predominantly set biogeographical patterns, and identifies `slow copiotrophs' subsisting at depth. Results demonstrate the predictability of the marine microbiome and connect ecological dynamics with carbon storage, crucial for projecting changes in a warming ocean.

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Section: Modeling Picoheterotrophic Functional Diversitysupporting

confidence: 90%

Predictable functional biogeography of marine microbial heterotrophs

Zakem,

McNichol,

Weissman

et al. 2024

Preprint

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“…Our approach used a statistical decomposition of variation in natural communities to reveal patterns in gene abundances. This approach is in contrast to a “mechanism-first” approach recently undertaken by Abreu et al ( 85 ) that identified the phenomenon of slow growth being favored at warmer temperatures in a simplified laboratory community, before searching for this pattern in the wild. While both approaches are powerful for understanding patterns in nature, a statistical approach allows us to discover patterns and processes without first postulating the environmental variables responsible or the details of the interaction in the laboratory.…”

Section: Discussionmentioning

confidence: 96%

Global patterns in gene content of soil microbiomes emerge from microbial interactions

Crocker,

Lee,

Chakraverti-Wuerthwein

et al. 2023

Preprint

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Microbial metabolism sustains life on Earth. Global sequencing surveys reveal ubiquitous patterns linking the taxa and genes responsible for metabolism with environmental conditions. One explanation for these patterns is environmental filtering: local conditions select for strains with particular traits. However, filtering assumes ecological interactions do not influence patterns. Here, we demonstrate a clear example of interactions driving global patterns in topsoil microbiomes. We find a global trade-off between two nitrate reduction genes and pH:nargene abundances increase whilenapdecreases with declining pH. Experiments contradict filtering by revealing strains possessing Nar thrive at low pH due to an interaction with Nap strains that mitigates nitrite toxicity. Thus, associations between environmental variables and gene abundances arise from environmentally modulated interactions driven by conserved metabolic traits.

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“…For example, selection might favour relatively rapid development (and therefore high metabolic rates [31,[83][84][85]) in cold environments (e.g. [86]) to reduce otherwise long generation times. Alternatively, or in addition, selection might favour low metabolic rates at high temperatures to increase otherwise low population carrying capacities [87][88][89][90].…”

Section: Discussionmentioning

confidence: 99%

Temperature and nutrition do not interact to shape the evolution of metabolic rate

Alton,

Kutz,

Bywater

et al. 2024

Phil. Trans. R. Soc. B

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Metabolic cold adaptation, or Krogh's rule, is the controversial hypothesis that predicts a monotonically negative relationship between metabolic rate and environmental temperature for ectotherms living along thermal clines measured at a common temperature. Macrophysiological patterns consistent with Krogh's rule are not always evident in nature, and experimentally evolved responses to temperature have failed to replicate such patterns. Hence, temperature may not be the sole driver of observed variation in metabolic rate. We tested the hypothesis that temperature, as a driver of energy demand, interacts with nutrition, a driver of energy supply, to shape the evolution of metabolic rate to produce a pattern resembling Krogh's rule. To do this, we evolved replicate lines of Drosophila melanogaster at 18, 25 or 28°C on control, low-calorie or low-protein diets. Contrary to our prediction, we observed no effect of nutrition, alone or interacting with temperature, on adult female and male metabolic rates. Moreover, support for Krogh's rule was only in females at lower temperatures. We, therefore, hypothesize that observed variation in metabolic rate along environmental clines arises from the metabolic consequences of environment-specific life-history optimization, rather than because of the direct effect of temperature on metabolic rate. This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.

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Warmer temperatures favor slower-growing bacteria in natural marine communities

Cited by 19 publications

References 84 publications

Predictable functional biogeography of marine microbial heterotrophs

Predictable functional biogeography of marine microbial heterotrophs

Global patterns in gene content of soil microbiomes emerge from microbial interactions

Temperature and nutrition do not interact to shape the evolution of metabolic rate

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