Exogenous nitrogen input skews estimates of microbial nitrogen use efficiency by ecoenzymatic stoichiometry
Background Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under exogenous nitrogen (N) input has never been clarified. Here, we investigated the effects of long-term N addition (as urea) on microbial N use effic...
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Published in | Ecological processes Vol. 12; no. 1; p. 46 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2023
Springer Nature B.V SpringerOpen |
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Abstract | Background
Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under exogenous nitrogen (N) input has never been clarified. Here, we investigated the effects of long-term N addition (as urea) on microbial N use efficiency (NUE), compared EEST and
18
O-labeling methods for determining NUE, and evaluated EEST’s theoretical assumption that the ratios of standard ecoenzymatic activities balance resource availability with microbial demand.
Results
We found that NUE estimated by EEST ranged from 0.94 to 0.98. In contrast, estimates of NUE by the
18
O-labeling method ranged from 0.07 to 0.30. The large differences in NUE values estimated by the two methods may be because the sum of β-N-acetylglucosaminidase and leucine aminopeptidase activities in the EEST model was not limited to microbial N acquisition under exogenous N inputs, resulting in an overestimation of microbial NUE by EEST. In addition, the acquisition of carbon by N-acquiring enzymes also likely interferes with the evaluation of NUE by EEST.
Conclusions
Our results demonstrate that caution must be exercised when using EEST to evaluate NUE under exogenous N inputs that may skew standard enzyme assays. |
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AbstractList | Abstract Background Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under exogenous nitrogen (N) input has never been clarified. Here, we investigated the effects of long-term N addition (as urea) on microbial N use efficiency (NUE), compared EEST and 18O-labeling methods for determining NUE, and evaluated EEST’s theoretical assumption that the ratios of standard ecoenzymatic activities balance resource availability with microbial demand. Results We found that NUE estimated by EEST ranged from 0.94 to 0.98. In contrast, estimates of NUE by the 18O-labeling method ranged from 0.07 to 0.30. The large differences in NUE values estimated by the two methods may be because the sum of β-N-acetylglucosaminidase and leucine aminopeptidase activities in the EEST model was not limited to microbial N acquisition under exogenous N inputs, resulting in an overestimation of microbial NUE by EEST. In addition, the acquisition of carbon by N-acquiring enzymes also likely interferes with the evaluation of NUE by EEST. Conclusions Our results demonstrate that caution must be exercised when using EEST to evaluate NUE under exogenous N inputs that may skew standard enzyme assays. BACKGROUND: Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under exogenous nitrogen (N) input has never been clarified. Here, we investigated the effects of long-term N addition (as urea) on microbial N use efficiency (NUE), compared EEST and ¹⁸O-labeling methods for determining NUE, and evaluated EEST’s theoretical assumption that the ratios of standard ecoenzymatic activities balance resource availability with microbial demand. RESULTS: We found that NUE estimated by EEST ranged from 0.94 to 0.98. In contrast, estimates of NUE by the ¹⁸O-labeling method ranged from 0.07 to 0.30. The large differences in NUE values estimated by the two methods may be because the sum of β-N-acetylglucosaminidase and leucine aminopeptidase activities in the EEST model was not limited to microbial N acquisition under exogenous N inputs, resulting in an overestimation of microbial NUE by EEST. In addition, the acquisition of carbon by N-acquiring enzymes also likely interferes with the evaluation of NUE by EEST. CONCLUSIONS: Our results demonstrate that caution must be exercised when using EEST to evaluate NUE under exogenous N inputs that may skew standard enzyme assays. BackgroundEcoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under exogenous nitrogen (N) input has never been clarified. Here, we investigated the effects of long-term N addition (as urea) on microbial N use efficiency (NUE), compared EEST and 18O-labeling methods for determining NUE, and evaluated EEST’s theoretical assumption that the ratios of standard ecoenzymatic activities balance resource availability with microbial demand.ResultsWe found that NUE estimated by EEST ranged from 0.94 to 0.98. In contrast, estimates of NUE by the 18O-labeling method ranged from 0.07 to 0.30. The large differences in NUE values estimated by the two methods may be because the sum of β-N-acetylglucosaminidase and leucine aminopeptidase activities in the EEST model was not limited to microbial N acquisition under exogenous N inputs, resulting in an overestimation of microbial NUE by EEST. In addition, the acquisition of carbon by N-acquiring enzymes also likely interferes with the evaluation of NUE by EEST.ConclusionsOur results demonstrate that caution must be exercised when using EEST to evaluate NUE under exogenous N inputs that may skew standard enzyme assays. Background Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under exogenous nitrogen (N) input has never been clarified. Here, we investigated the effects of long-term N addition (as urea) on microbial N use efficiency (NUE), compared EEST and 18 O-labeling methods for determining NUE, and evaluated EEST’s theoretical assumption that the ratios of standard ecoenzymatic activities balance resource availability with microbial demand. Results We found that NUE estimated by EEST ranged from 0.94 to 0.98. In contrast, estimates of NUE by the 18 O-labeling method ranged from 0.07 to 0.30. The large differences in NUE values estimated by the two methods may be because the sum of β-N-acetylglucosaminidase and leucine aminopeptidase activities in the EEST model was not limited to microbial N acquisition under exogenous N inputs, resulting in an overestimation of microbial NUE by EEST. In addition, the acquisition of carbon by N-acquiring enzymes also likely interferes with the evaluation of NUE by EEST. Conclusions Our results demonstrate that caution must be exercised when using EEST to evaluate NUE under exogenous N inputs that may skew standard enzyme assays. |
ArticleNumber | 46 |
Author | Sun, Lifei Moorhead, Daryl L. Li, Shuailin Wang, Chao Cui, Yongxing Wanek, Wolfgang |
Author_xml | – sequence: 1 givenname: Lifei surname: Sun fullname: Sun, Lifei email: sunlifei@iae.ac.cn organization: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences – sequence: 2 givenname: Daryl L. surname: Moorhead fullname: Moorhead, Daryl L. organization: Department of Environmental Sciences, University of Toledo – sequence: 3 givenname: Yongxing surname: Cui fullname: Cui, Yongxing email: cuiyongxing.cn@gmail.com organization: Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University – sequence: 4 givenname: Wolfgang surname: Wanek fullname: Wanek, Wolfgang organization: Division of Terrestrial Ecosystem Research, Center of Microbiology and Environmental Systems Science, University of Vienna – sequence: 5 givenname: Shuailin surname: Li fullname: Li, Shuailin organization: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences – sequence: 6 givenname: Chao surname: Wang fullname: Wang, Chao organization: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences |
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Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under... BackgroundEcoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under... BACKGROUND: Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models under... Abstract Background Ecoenzymatic stoichiometry models (EEST) are often used to evaluate microbial nutrient use efficiency, but the validity of these models... |
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SubjectTerms | Aminopeptidase carbon Earth and Environmental Science Efficiency Environment Estimates Extracellular enzyme Isotope labeling Labeling Leucine leucyl aminopeptidase Microbial metabolism limitation microbial nitrogen Microorganisms N-Acetylglucosaminidase Nitrogen Nitrogen addition nutrient use efficiency Resource allocation Resource availability Stoichiometry Urea |
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Title | Exogenous nitrogen input skews estimates of microbial nitrogen use efficiency by ecoenzymatic stoichiometry |
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