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Specific microbial gene abundances and soil parameters contribute to C, N, and greenhouse gas process rates after land use change in Southern Amazonian Soils.

Lammel DR, Feigl BJ, Cerri CC, Nüsslein K - Front Microbiol (2015)

Bottom Line: Methanogens (mcrA) and methanotrophs (pmoA) were more abundant in forest soil, but methane flux was highest in pasture sites, which was related to soil compaction.Rather than analyzing direct correlations, the data integration using multivariate tools provided a better overview of biogeochemical processes.Overall, in the wet season, land use change from forest to agriculture reduced the abundance of different functional microbial groups related to the soil C and N cycles; integrating the gene abundance data and soil parameters provided a comprehensive overview of these interactions.

View Article: PubMed Central - PubMed

Affiliation: Centro de Energia Nuclear na Agricultura, University of São Paulo Piracicaba, Brazil ; Department of Microbiology, University of Massachusetts Amherst, MA, USA.

ABSTRACT
Ecological processes regulating soil carbon (C) and nitrogen (N) cycles are still poorly understood, especially in the world's largest agricultural frontier in Southern Amazonia. We analyzed soil parameters in samples from pristine rainforest and after land use change to pasture and crop fields, and correlated them with abundance of functional and phylogenetic marker genes (amoA, nirK, nirS, norB, nosZ, nifH, mcrA, pmoA, and 16S/18S rRNA). Additionally, we integrated these parameters using path analysis and multiple regressions. Following forest removal, concentrations of soil C and N declined, and pH and nutrient levels increased, which influenced microbial abundances and biogeochemical processes. A seasonal trend was observed, suggesting that abundances of microbial groups were restored to near native levels after the dry winter fallow. Integration of the marker gene abundances with soil parameters using path analysis and multiple regressions provided good predictions of biogeochemical processes, such as the fluxes of NO3, N2O, CO2, and CH4. In the wet season, agricultural soil showed the highest abundance of nitrifiers (amoA) and Archaea, however, forest soils showed the highest abundances of denitrifiers (nirK, nosZ) and high N, which correlated with increased N2O emissions. Methanogens (mcrA) and methanotrophs (pmoA) were more abundant in forest soil, but methane flux was highest in pasture sites, which was related to soil compaction. Rather than analyzing direct correlations, the data integration using multivariate tools provided a better overview of biogeochemical processes. Overall, in the wet season, land use change from forest to agriculture reduced the abundance of different functional microbial groups related to the soil C and N cycles; integrating the gene abundance data and soil parameters provided a comprehensive overview of these interactions. Path analysis and multiple regressions addressed the need for more comprehensive approaches to improve our mechanistic understanding of biogeochemical cycles.

No MeSH data available.


Related in: MedlinePlus

Path analysis diagrams for microbial gene abundances and CO2 fluxes (A); for methane dynamics (B); and for N-cycle related processes, NH4+ prediction (C), NO3- prediction (D) and N2O fluxes (E). The diagrams show the relationships among the selected variables and the influence of soil chemical factors in soils across land use types in Southern Amazonia in the middle of the wet season (for more information please see the Methods section). The numbers listed within arrows are standardized path coefficients (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, or x P < x). The numbers on the top of the variable boxes represent unexplained variation (1 – R2) which represents the effect of unmeasured variables and measurement error. The litter C and N data are from Lammel et al. (2015).
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Figure 1: Path analysis diagrams for microbial gene abundances and CO2 fluxes (A); for methane dynamics (B); and for N-cycle related processes, NH4+ prediction (C), NO3- prediction (D) and N2O fluxes (E). The diagrams show the relationships among the selected variables and the influence of soil chemical factors in soils across land use types in Southern Amazonia in the middle of the wet season (for more information please see the Methods section). The numbers listed within arrows are standardized path coefficients (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, or x P < x). The numbers on the top of the variable boxes represent unexplained variation (1 – R2) which represents the effect of unmeasured variables and measurement error. The litter C and N data are from Lammel et al. (2015).

Mentions: To further investigate the interaction among the variables, two techniques were used to test models with these variables and to explain the soil respiration. The first was path analysis, which tested selected combinations of the variables based on an initial full model (Supplementary Figure S1). That initial full model was reduced to a significant model where all the paths were significant (Figure 1A). In this path diagram, the soil C negatively influenced Archaeal abundance, the soil pH negatively influenced Bacterial abundance, and all of them in addition to OM influenced the CO2 flux. The numbers on the top of each variable boxes represent unexplained variation (1 – R2), which represents the effect of unmeasured variables and measurement error (Petersen et al., 2012). This means that in this model only 0.23 of the CO2 Flux was not explained by this model; and that Archaea and Bacteria contributed a little to the model, with 0.83 and 0.63 of their variance not explained by this model. The other technique used was stepwise multiple regressions, and in this case, there was no guided dependency among variables as that stated in the path analysis (Supplementary Information “Regressions”). Full models were than tested and reduced for models where all the coefficients were statically significant and the best Akaike information criterion (AIC) achieved. The coefficients of the best analysis were then used in a linear regression against the CO2 flux, and achieved an R2 of 0.97 and P < 0.001 (Figure 2A). Both independent techniques showed that the abundance of 16S genes of Archaea and Bacteria contributed to the explanation of the CO2 fluxes from the analyzed samples.


Specific microbial gene abundances and soil parameters contribute to C, N, and greenhouse gas process rates after land use change in Southern Amazonian Soils.

Lammel DR, Feigl BJ, Cerri CC, Nüsslein K - Front Microbiol (2015)

Path analysis diagrams for microbial gene abundances and CO2 fluxes (A); for methane dynamics (B); and for N-cycle related processes, NH4+ prediction (C), NO3- prediction (D) and N2O fluxes (E). The diagrams show the relationships among the selected variables and the influence of soil chemical factors in soils across land use types in Southern Amazonia in the middle of the wet season (for more information please see the Methods section). The numbers listed within arrows are standardized path coefficients (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, or x P < x). The numbers on the top of the variable boxes represent unexplained variation (1 – R2) which represents the effect of unmeasured variables and measurement error. The litter C and N data are from Lammel et al. (2015).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4594008&req=5

Figure 1: Path analysis diagrams for microbial gene abundances and CO2 fluxes (A); for methane dynamics (B); and for N-cycle related processes, NH4+ prediction (C), NO3- prediction (D) and N2O fluxes (E). The diagrams show the relationships among the selected variables and the influence of soil chemical factors in soils across land use types in Southern Amazonia in the middle of the wet season (for more information please see the Methods section). The numbers listed within arrows are standardized path coefficients (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, or x P < x). The numbers on the top of the variable boxes represent unexplained variation (1 – R2) which represents the effect of unmeasured variables and measurement error. The litter C and N data are from Lammel et al. (2015).
Mentions: To further investigate the interaction among the variables, two techniques were used to test models with these variables and to explain the soil respiration. The first was path analysis, which tested selected combinations of the variables based on an initial full model (Supplementary Figure S1). That initial full model was reduced to a significant model where all the paths were significant (Figure 1A). In this path diagram, the soil C negatively influenced Archaeal abundance, the soil pH negatively influenced Bacterial abundance, and all of them in addition to OM influenced the CO2 flux. The numbers on the top of each variable boxes represent unexplained variation (1 – R2), which represents the effect of unmeasured variables and measurement error (Petersen et al., 2012). This means that in this model only 0.23 of the CO2 Flux was not explained by this model; and that Archaea and Bacteria contributed a little to the model, with 0.83 and 0.63 of their variance not explained by this model. The other technique used was stepwise multiple regressions, and in this case, there was no guided dependency among variables as that stated in the path analysis (Supplementary Information “Regressions”). Full models were than tested and reduced for models where all the coefficients were statically significant and the best Akaike information criterion (AIC) achieved. The coefficients of the best analysis were then used in a linear regression against the CO2 flux, and achieved an R2 of 0.97 and P < 0.001 (Figure 2A). Both independent techniques showed that the abundance of 16S genes of Archaea and Bacteria contributed to the explanation of the CO2 fluxes from the analyzed samples.

Bottom Line: Methanogens (mcrA) and methanotrophs (pmoA) were more abundant in forest soil, but methane flux was highest in pasture sites, which was related to soil compaction.Rather than analyzing direct correlations, the data integration using multivariate tools provided a better overview of biogeochemical processes.Overall, in the wet season, land use change from forest to agriculture reduced the abundance of different functional microbial groups related to the soil C and N cycles; integrating the gene abundance data and soil parameters provided a comprehensive overview of these interactions.

View Article: PubMed Central - PubMed

Affiliation: Centro de Energia Nuclear na Agricultura, University of São Paulo Piracicaba, Brazil ; Department of Microbiology, University of Massachusetts Amherst, MA, USA.

ABSTRACT
Ecological processes regulating soil carbon (C) and nitrogen (N) cycles are still poorly understood, especially in the world's largest agricultural frontier in Southern Amazonia. We analyzed soil parameters in samples from pristine rainforest and after land use change to pasture and crop fields, and correlated them with abundance of functional and phylogenetic marker genes (amoA, nirK, nirS, norB, nosZ, nifH, mcrA, pmoA, and 16S/18S rRNA). Additionally, we integrated these parameters using path analysis and multiple regressions. Following forest removal, concentrations of soil C and N declined, and pH and nutrient levels increased, which influenced microbial abundances and biogeochemical processes. A seasonal trend was observed, suggesting that abundances of microbial groups were restored to near native levels after the dry winter fallow. Integration of the marker gene abundances with soil parameters using path analysis and multiple regressions provided good predictions of biogeochemical processes, such as the fluxes of NO3, N2O, CO2, and CH4. In the wet season, agricultural soil showed the highest abundance of nitrifiers (amoA) and Archaea, however, forest soils showed the highest abundances of denitrifiers (nirK, nosZ) and high N, which correlated with increased N2O emissions. Methanogens (mcrA) and methanotrophs (pmoA) were more abundant in forest soil, but methane flux was highest in pasture sites, which was related to soil compaction. Rather than analyzing direct correlations, the data integration using multivariate tools provided a better overview of biogeochemical processes. Overall, in the wet season, land use change from forest to agriculture reduced the abundance of different functional microbial groups related to the soil C and N cycles; integrating the gene abundance data and soil parameters provided a comprehensive overview of these interactions. Path analysis and multiple regressions addressed the need for more comprehensive approaches to improve our mechanistic understanding of biogeochemical cycles.

No MeSH data available.


Related in: MedlinePlus