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240909s2024 xx |||||o 00| ||eng c |
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|a 10.1111/gcb.17492
|2 doi
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|a pubmed24n1528.xml
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|a (DE-627)NLM377355372
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|a (NLM)39248442
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Luo, Zhaoyang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Temperature controls the relation between soil organic carbon and microbial carbon use efficiency
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| 264 |
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|c 2024
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 09.09.2024
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|a Date Revised 09.09.2024
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a © 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.
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|a Microbial carbon use efficiency (CUE) is an important variable mediating microbial effects on soil organic carbon (SOC) since it summarizes how much carbon is used for microbial growth or is respired. Yet, the role of CUE in regulating SOC storage remains debated, with evidence for both positive and negative SOC-CUE relations. Here, we use a combination of measured data around the world and numerical simulations to explore SOC-CUE relations accounting for temperature (T) effects on CUE. Results reveal that the sign of the CUE-T relation controls the direction of the SOC-CUE relations. A negative CUE-T relation leads to a positive SOC-CUE relation and vice versa, highlighting that CUE-T patterns significantly affect how organic carbon is used by microbes and hence SOC-CUE relations. Numerical results also confirm the observed negative SOC-T relation, regardless of the CUE-T patterns, implying that temperature plays a more dominant role than CUE in controlling SOC storage. The SOC-CUE relation is usually negative when temperature effects are isolated, even though it can become positive when nonlinear microbial turnover is considered. These results indicate a dominant role of CUE-T patterns in controlling the SOC-CUE relation. Our findings help to better understand SOC and microbial responses to a warming climate
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4 |
|a Journal Article
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| 650 |
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4 |
|a climate warming
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| 650 |
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4 |
|a data analysis
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| 650 |
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4 |
|a microbial carbon use efficiency
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| 650 |
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4 |
|a numerical modeling
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| 650 |
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4 |
|a soil biogeochemistry
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| 650 |
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4 |
|a soil carbon modeling
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| 650 |
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7 |
|a Carbon
|2 NLM
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| 650 |
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7 |
|a 7440-44-0
|2 NLM
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| 650 |
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7 |
|a Soil
|2 NLM
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| 700 |
1 |
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|a Ren, Jianning
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Manzoni, Stefano
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fatichi, Simone
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Global change biology
|d 1999
|g 30(2024), 9 vom: 09. Sept., Seite e17492
|w (DE-627)NLM098239996
|x 1365-2486
|7 nnns
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| 773 |
1 |
8 |
|g volume:30
|g year:2024
|g number:9
|g day:09
|g month:09
|g pages:e17492
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1111/gcb.17492
|3 Volltext
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|a AR
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| 952 |
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|d 30
|j 2024
|e 9
|b 09
|c 09
|h e17492
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