Increasing temperatures and variability of precipitation events due to climate change will lead in the future to higher irrigation demands in agroecosystems.However,the use of secondary treated wasterwater(TWW)could h...Increasing temperatures and variability of precipitation events due to climate change will lead in the future to higher irrigation demands in agroecosystems.However,the use of secondary treated wasterwater(TWW)could have consequences for the receiving soil environment and its resident microbial communities.The objective of this study was to characterize the importance of soil properties and habitats to the response of soil bacteria and archaea to irrigation with TWW.Two agricultural soils with contrasting textures(loamy sand or silt loam)and,for each,three variants differing in soil organic carbon and nitrogen,as generated by long-term fertilization,were analyzed.For each of these six soils,prokaryotic communities from two habitats,i.e.,root-free bulk soil and the rhizosphere of developing cucumber plants in the greenhouse,were characterized.Communities were analyzed by the quantity and diversity of their polymerase chain reaction(PCR)-amplified 16S rRNA genes.To account for TWW-associated nutrient effects,potable water(PW)served as a control.Amplicon sequence analysis showed that prokaryotic communities mainly consisted of bacteria(99.8%).Upon irrigation,regardless of the water quality,prokaryotic diversity declined,p H increased,and no bacterial growth was detected in bulk soil.In contrast,the growth of cucumbers was stimulated by TWW,indicating that plants were the main beneficiaries.Moreover,strong responses were seen in the rhizosphere,suggesting an indirect effect of TWW by altered rhizodepositions.The main bacterial responders to TWW were Proteobacteria,Bacteroidetes,Actinobacteria,and Planctomycetes.Changes in bacterial communities due to TWW were less pronounced in all variants of the silt loam,indicating the importance of clay and soil organic carbon for buffering effects of TWW on soil bacterial communities.Hence,soil organic carbon and soil texture are important parameters that need to be considered when applying TWW in agriculture.展开更多
Refined conversion factors for soil fungal biomarkers are proposed.High interspecific variability is present in all fungal biomarkers.A modeling approach supports the validity of biomarker estimates in diverse soils.I...Refined conversion factors for soil fungal biomarkers are proposed.High interspecific variability is present in all fungal biomarkers.A modeling approach supports the validity of biomarker estimates in diverse soils.ITS1 copies vary strongly,but are fungal-specific with least phylogenetic bias.A combination of fungal biomarkers will reveal soil fungal physiology and activity.The abundances of fungi and bacteria in soil are used as simple predictors for carbon dynamics,and represent widely available microbial traits.Soil biomarkers serve as quantitative estimates of these microbial groups,though not quantifying microbial biomass per se.The accurate conversion to microbial carbon pools,and an understanding of its comparability among soils is therefore needed.We refined conversion factors for classical fungal biomarkers,and evaluated the application of quantitative PCR(qPCR,rDNA copies)as a biomarker for soil fungi.Based on biomarker contents in pure fungal cultures of 30 isolates tested here,combined with comparable published datasets,we propose average conversion factors of 95.3 g fungal C g^(−1) ergosterol,32.0 mg fungal Cμmol−1 PLFA 18:2ω6,9 and 0.264 pg fungal C ITS1 DNA copy−1.As expected,interspecific variability was most pronounced in rDNA copies,though qPCR results showed the least phylogenetic bias.A modeling approach based on exemplary agricultural soils further supported the hypothesis that high diversity in soil buffers against biomarker variability,whereas also phylogenetic biases impact the accuracy of comparisons in biomarker estimates.Our analyses suggest that qPCR results cover the fungal community in soil best,though with a variability only partly offset in highly diverse soils.PLFA 18:2ω6,9 and ergosterol represent accurate biomarkers to quantify Ascomycota and Basidiomycota.To conclude,the ecological interpretation and coverage of biomarker data prior to their application in global models is important,where the combination of different biomarkers may be most insightful.展开更多
基金financially supported by the German Federal Ministry for Food and Agriculture (BMEL) based on the decision of the Parliament of the Federal Republic of Germany (No. 813IL01).
文摘Increasing temperatures and variability of precipitation events due to climate change will lead in the future to higher irrigation demands in agroecosystems.However,the use of secondary treated wasterwater(TWW)could have consequences for the receiving soil environment and its resident microbial communities.The objective of this study was to characterize the importance of soil properties and habitats to the response of soil bacteria and archaea to irrigation with TWW.Two agricultural soils with contrasting textures(loamy sand or silt loam)and,for each,three variants differing in soil organic carbon and nitrogen,as generated by long-term fertilization,were analyzed.For each of these six soils,prokaryotic communities from two habitats,i.e.,root-free bulk soil and the rhizosphere of developing cucumber plants in the greenhouse,were characterized.Communities were analyzed by the quantity and diversity of their polymerase chain reaction(PCR)-amplified 16S rRNA genes.To account for TWW-associated nutrient effects,potable water(PW)served as a control.Amplicon sequence analysis showed that prokaryotic communities mainly consisted of bacteria(99.8%).Upon irrigation,regardless of the water quality,prokaryotic diversity declined,p H increased,and no bacterial growth was detected in bulk soil.In contrast,the growth of cucumbers was stimulated by TWW,indicating that plants were the main beneficiaries.Moreover,strong responses were seen in the rhizosphere,suggesting an indirect effect of TWW by altered rhizodepositions.The main bacterial responders to TWW were Proteobacteria,Bacteroidetes,Actinobacteria,and Planctomycetes.Changes in bacterial communities due to TWW were less pronounced in all variants of the silt loam,indicating the importance of clay and soil organic carbon for buffering effects of TWW on soil bacterial communities.Hence,soil organic carbon and soil texture are important parameters that need to be considered when applying TWW in agriculture.
基金funding by the Deutsche Forschungsgemeinschaft(DFG,grant number 465123751,SPP2322 SoilSystems)supported by DFG grant HE 6183/5-1 and SM by MA4436/1-5.
文摘Refined conversion factors for soil fungal biomarkers are proposed.High interspecific variability is present in all fungal biomarkers.A modeling approach supports the validity of biomarker estimates in diverse soils.ITS1 copies vary strongly,but are fungal-specific with least phylogenetic bias.A combination of fungal biomarkers will reveal soil fungal physiology and activity.The abundances of fungi and bacteria in soil are used as simple predictors for carbon dynamics,and represent widely available microbial traits.Soil biomarkers serve as quantitative estimates of these microbial groups,though not quantifying microbial biomass per se.The accurate conversion to microbial carbon pools,and an understanding of its comparability among soils is therefore needed.We refined conversion factors for classical fungal biomarkers,and evaluated the application of quantitative PCR(qPCR,rDNA copies)as a biomarker for soil fungi.Based on biomarker contents in pure fungal cultures of 30 isolates tested here,combined with comparable published datasets,we propose average conversion factors of 95.3 g fungal C g^(−1) ergosterol,32.0 mg fungal Cμmol−1 PLFA 18:2ω6,9 and 0.264 pg fungal C ITS1 DNA copy−1.As expected,interspecific variability was most pronounced in rDNA copies,though qPCR results showed the least phylogenetic bias.A modeling approach based on exemplary agricultural soils further supported the hypothesis that high diversity in soil buffers against biomarker variability,whereas also phylogenetic biases impact the accuracy of comparisons in biomarker estimates.Our analyses suggest that qPCR results cover the fungal community in soil best,though with a variability only partly offset in highly diverse soils.PLFA 18:2ω6,9 and ergosterol represent accurate biomarkers to quantify Ascomycota and Basidiomycota.To conclude,the ecological interpretation and coverage of biomarker data prior to their application in global models is important,where the combination of different biomarkers may be most insightful.
