2023
DOI: 10.1038/s41467-023-41096-x
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Quantifying thermal adaptation of soil microbial respiration

Charlotte J. Alster,
Allycia van de Laar,
Jordan P. Goodrich
et al.

Abstract: Quantifying the rate of thermal adaptation of soil microbial respiration is essential in determining potential for carbon cycle feedbacks under a warming climate. Uncertainty surrounding this topic stems in part from persistent methodological issues and difficulties isolating the interacting effects of changes in microbial community responses from changes in soil carbon availability. Here, we constructed a series of temperature response curves of microbial respiration (given unlimited substrate) using soils sa… Show more

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Cited by 15 publications
(4 citation statements)
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“…The intrinsic temperature sensitivity of soil microbes is challenging to alter over time scales ranging from weeks to decades, despite their rapid adaptability under harsh conditions (Alster et al., 2023; Walker et al., 2018). Microbial physiological processes are sustained by changes in microbial biomass, which are influenced by the intrinsic temperature sensitivity of soil microbes (Karhu et al., 2014; Simon et al., 2020).…”
Section: Discussionmentioning
confidence: 99%
“…The intrinsic temperature sensitivity of soil microbes is challenging to alter over time scales ranging from weeks to decades, despite their rapid adaptability under harsh conditions (Alster et al., 2023; Walker et al., 2018). Microbial physiological processes are sustained by changes in microbial biomass, which are influenced by the intrinsic temperature sensitivity of soil microbes (Karhu et al., 2014; Simon et al., 2020).…”
Section: Discussionmentioning
confidence: 99%
“…The residual standard errors indicate that Ratkowsky 1983 is a more appropriate fit for our data of growth rate over temperature compared to MMRT. However, we are particularly interested in MMRT due to its underlying thermodynamic theory, as well as its application in soil ecosystems ( 19 , 20 , 52 ).…”
Section: Discussionmentioning
confidence: 99%
“…where A is the slope and B is the value of at T 0 ( 20 ). We used a non-linear least-squares regression in R version 4.2.1 to fit MMRT and calculated the Topt and Tinf numerically using the first and second derivatives, respectively.…”
Section: Methodsmentioning
confidence: 99%
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