Nannipieri, P.

Penton, C.R.

Purahong, W.

Schloter, M.

van Elsas, J.D.

Penton, C. R.

van Elsas, J. D.

University of Groningen

   University of GroningenRecommendations for soil microbiome analysesNannipieri, P.; Penton, C. R.; Purahong, W.; Schloter, M.; van Elsas, J. D.Published in:Biology and Fertility of SoilsDOI:10.1007/s00374-019-01409-zIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2019Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Nannipieri, P., Penton, C. R., Purahong, W., Schloter, M., & van Elsas, J. D. (2019). Recommendations forsoil microbiome analyses. Biology and Fertility of Soils , 55(8), 765-766. https://doi.org/10.1007/s00374-019-01409-zCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 29-04-2023EDITORIALRecommendations for soil microbiome analysesP. Nannipieri1 & C. R. Penton2 & W. Purahong3 & M. Schloter4 & J. D. van Elsas5# Springer-Verlag GmbH Germany, part of Springer Nature 2019As a result of the recent developments in high-throughputmolecular biology methods, our understanding of the soilmicrobiome has increased significantly over the last decades.Using microbiome DNA- or RNA-based analyses, such asamplicon sequencing and shotgun metagenomics, a largenumber of new phyla could be identified and some could beassigned to important traits, which drive keystone functionsfor ecosystem services provided by soils. However, there isstill a lack of information with respect to the major drivers thattrigger microbial community composition and function on allscales, i.e., from the pedon (microhabitat, at the micrometerscale) to the region. A large number of publications that in-cluded microbiome analyses have been submitted in the lastyear’s Biology and Fertility of Soils, and some have addressedsuch questions. However, in many cases, the provided infor-mation, despite the studies being sound in analytics, did notstrongly contribute to our understanding of the impact on, aswell as the function of, the soil microbiome for severalreasons.1) As a result of the high costs for analyses and the complexbioinformatics analysis, only one sampling time point isoften chosen for analysis, although there is a large body ofevidence on the high temporal dynamics of the soilmicrobiome, in particular when activity hotspots like therhizosphere, the mycosphere, or the detritusphere are con-sidered. Changes over time can be higher in magnitudethan those observed between treatments at a single sam-pling point, for instance due to differences in manage-ment, plant cover, and other variables (Nannipieri et al.2019). In many cases, samples from agricultural soils aretaken at crop harvest time. Thus, such studies mostly de-scribe the response of the soil microbiome to the distur-bance (and stress) caused by the harvest instead ofdepicting the typical response pattern and dynamics ofthe soil microbiome. It is deemed essential to select atleast two sampling time points, with at least one incurringa minimal disturbance (mostly in spring), using this as areference for the analysis of management-related factorslike fertilization, tillage, or cropping sequence.2) Besides the temporal dynamics of soil microbiomes, thereis also a huge heterogeneity in space, and so intricatestatistical techniques are required to grasp this variation.However, the multiple soil samples taken from individualfield plots are often composited to yield just one samplecovering a plot. In more elaborate studies, spatial hetero-geneity should be captured in order to statistically com-pare variances with plot and treatment differences, usingan appropriate number of replicates. Experimental de-signs with low number of replicates (e.g., three) reducethe statistical power of both univariate and multivariatestatistics.3) Linking microbial community composition or potentialfunction to the transformation processes that occur inthe soil is often performed only taking into account theconcentration of the product/substrate of the target pro-cess, ignoring the fact that the levels of these compoundsin the soil may also be dependent on other biotic/abioticprocesses. For example, changes in the concentration ofexchangeable ammonium or nitrate depend not only onthe activity of autotrophic nitrification but also on theactivity of other N cycling processes. Therefore, targetprocess rates should be determined to establish such link-ages and the quantification and/or presence/absence offunctional guilds should be included in the discussion.* P. Nannipieripaolo.nannipieri@unifi.it1 Department of Agriculture, Food, Environment and Forestry,University of Firenze, Florence, Italy2 College of Integrative Sciences and Arts, Arizona State University,Mesa, AZ, USA3 Department of Soil Ecology, UFZ-Helmholtz Centre forEnvironmental Research, Halle (Saale), Germany4 Research Unit for Comparative Microbiome Analysis, HelmholtzCenter for Environment and Health, Oberschleissheim, Germany5 Department of Microbial Ecology, GELIFES, University ofGroningen, Groningen, Netherlandshttps://doi.org/10.1007/s00374-019-01409-zBiology and Fertility of Soils (2019) 55:765–766Published online: 30            October     2   0194) The detection or quantification of a target gene is oftendirectly linked to a process in which the predicted proteinis presumed to be a central catalyst. However, it is impor-tant to consider and discuss that such a presumption isrisky, as the predicted protein may or may not be activeand be a central player within the microbial community.As a second confounding factor, using microbiome DNAas a proxy for potential function is often biased due to thepresence of DNA from dead microbes.5) Data generated by amplicon sequencing (coined by someauthors as “barcoding” or “metabarcoding”) and shotgunmetagenomics constitute “relative” data. Such data, bydefinition, do not provide any information on the totalabundances of the identified clades. Thus, mainly in soilsystems in which changes of microbial biomass occur,including for key organisms or functions, ribosomalRNA gene–based qPCR analyses are needed to defineabsolute abundances, which could serve as proxies forpotential transformation rates.6) The Earth Microbiome project (www.earthmicrobiome.org) has provided a number of important standards forthe analysis of soil microbiomes, which are very helpfulif the data from single projects are to be compared withstudies of relevance from other researchers in a meta-analysis. If no clear arguments exist against thesestandards, they should be implemented into allanalytical pipelines as soon as possible.We strongly recommend that the aforementioned six issuesare implemented in all future manuscripts submitted toBiology and Fertility of Soils, as has been advocated andsummarized in two recent papers (Schöler et al. 2017;Vestergaard et al. 2017).ReferencesNannipieri P, Ascher-Jenull J, Ceccherini MT, Pietramellara G, RenellaG, Schloter M (2019) Beyond microbial diversity for predicting soilfunctions. Pedosphere 29, in pressSchöler A, Jacquiod S, Vestergaard G, Schulz S, Schloter M (2017)Analysis of soil microbial communities based on amplicon sequenc-ing of marker genes. Biol Fertil Soils 53:485–489Vestergaard G, Schulz S, Schöler A, Schloter M (2017) Making big datasmart - how to use metagenomics to understand soil quality. BiolFertil Soils 53:479–484Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.Biol Fertil Soils (2019) 55:765–766766

English

Recommendations for soil microbiome analyses

NARCIS 

   University of GroningenRecommendations for soil microbiome analysesNannipieri, P.; Penton, C. R.; Purahong, W.; Schloter, M.; van Elsas, J. D.Published in:Biology and Fertility of SoilsDOI:10.1007/s00374-019-01409-zIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2019Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Nannipieri, P., Penton, C. R., Purahong, W., Schloter, M., & van Elsas, J. D. (2019). Recommendations forsoil microbiome analyses. Biology and Fertility of Soils , 55(8), 765-766. https://doi.org/10.1007/s00374-019-01409-zCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 19-11-2022EDITORIALRecommendations for soil microbiome analysesP. Nannipieri1 & C. R. Penton2 & W. Purahong3 & M. Schloter4 & J. D. van Elsas5# Springer-Verlag GmbH Germany, part of Springer Nature 2019As a result of the recent developments in high-throughputmolecular biology methods, our understanding of the soilmicrobiome has increased significantly over the last decades.Using microbiome DNA- or RNA-based analyses, such asamplicon sequencing and shotgun metagenomics, a largenumber of new phyla could be identified and some could beassigned to important traits, which drive keystone functionsfor ecosystem services provided by soils. However, there isstill a lack of information with respect to the major drivers thattrigger microbial community composition and function on allscales, i.e., from the pedon (microhabitat, at the micrometerscale) to the region. A large number of publications that in-cluded microbiome analyses have been submitted in the lastyear’s Biology and Fertility of Soils, and some have addressedsuch questions. However, in many cases, the provided infor-mation, despite the studies being sound in analytics, did notstrongly contribute to our understanding of the impact on, aswell as the function of, the soil microbiome for severalreasons.1) As a result of the high costs for analyses and the complexbioinformatics analysis, only one sampling time point isoften chosen for analysis, although there is a large body ofevidence on the high temporal dynamics of the soilmicrobiome, in particular when activity hotspots like therhizosphere, the mycosphere, or the detritusphere are con-sidered. Changes over time can be higher in magnitudethan those observed between treatments at a single sam-pling point, for instance due to differences in manage-ment, plant cover, and other variables (Nannipieri et al.2019). In many cases, samples from agricultural soils aretaken at crop harvest time. Thus, such studies mostly de-scribe the response of the soil microbiome to the distur-bance (and stress) caused by the harvest instead ofdepicting the typical response pattern and dynamics ofthe soil microbiome. It is deemed essential to select atleast two sampling time points, with at least one incurringa minimal disturbance (mostly in spring), using this as areference for the analysis of management-related factorslike fertilization, tillage, or cropping sequence.2) Besides the temporal dynamics of soil microbiomes, thereis also a huge heterogeneity in space, and so intricatestatistical techniques are required to grasp this variation.However, the multiple soil samples taken from individualfield plots are often composited to yield just one samplecovering a plot. In more elaborate studies, spatial hetero-geneity should be captured in order to statistically com-pare variances with plot and treatment differences, usingan appropriate number of replicates. Experimental de-signs with low number of replicates (e.g., three) reducethe statistical power of both univariate and multivariatestatistics.3) Linking microbial community composition or potentialfunction to the transformation processes that occur inthe soil is often performed only taking into account theconcentration of the product/substrate of the target pro-cess, ignoring the fact that the levels of these compoundsin the soil may also be dependent on other biotic/abioticprocesses. For example, changes in the concentration ofexchangeable ammonium or nitrate depend not only onthe activity of autotrophic nitrification but also on theactivity of other N cycling processes. Therefore, targetprocess rates should be determined to establish such link-ages and the quantification and/or presence/absence offunctional guilds should be included in the discussion.* P. Nannipieripaolo.nannipieri@unifi.it1 Department of Agriculture, Food, Environment and Forestry,University of Firenze, Florence, Italy2 College of Integrative Sciences and Arts, Arizona State University,Mesa, AZ, USA3 Department of Soil Ecology, UFZ-Helmholtz Centre forEnvironmental Research, Halle (Saale), Germany4 Research Unit for Comparative Microbiome Analysis, HelmholtzCenter for Environment and Health, Oberschleissheim, Germany5 Department of Microbial Ecology, GELIFES, University ofGroningen, Groningen, Netherlandshttps://doi.org/10.1007/s00374-019-01409-zBiology and Fertility of Soils (2019) 55:765–766Published online: 30            October     2   0194) The detection or quantification of a target gene is oftendirectly linked to a process in which the predicted proteinis presumed to be a central catalyst. However, it is impor-tant to consider and discuss that such a presumption isrisky, as the predicted protein may or may not be activeand be a central player within the microbial community.As a second confounding factor, using microbiome DNAas a proxy for potential function is often biased due to thepresence of DNA from dead microbes.5) Data generated by amplicon sequencing (coined by someauthors as “barcoding” or “metabarcoding”) and shotgunmetagenomics constitute “relative” data. Such data, bydefinition, do not provide any information on the totalabundances of the identified clades. Thus, mainly in soilsystems in which changes of microbial biomass occur,including for key organisms or functions, ribosomalRNA gene–based qPCR analyses are needed to defineabsolute abundances, which could serve as proxies forpotential transformation rates.6) The Earth Microbiome project (www.earthmicrobiome.org) has provided a number of important standards forthe analysis of soil microbiomes, which are very helpfulif the data from single projects are to be compared withstudies of relevance from other researchers in a meta-analysis. If no clear arguments exist against thesestandards, they should be implemented into allanalytical pipelines as soon as possible.We strongly recommend that the aforementioned six issuesare implemented in all future manuscripts submitted toBiology and Fertility of Soils, as has been advocated andsummarized in two recent papers (Schöler et al. 2017;Vestergaard et al. 2017).ReferencesNannipieri P, Ascher-Jenull J, Ceccherini MT, Pietramellara G, RenellaG, Schloter M (2019) Beyond microbial diversity for predicting soilfunctions. Pedosphere 29, in pressSchöler A, Jacquiod S, Vestergaard G, Schulz S, Schloter M (2017)Analysis of soil microbial communities based on amplicon sequenc-ing of marker genes. Biol Fertil Soils 53:485–489Vestergaard G, Schulz S, Schöler A, Schloter M (2017) Making big datasmart - how to use metagenomics to understand soil quality. BiolFertil Soils 53:479–484Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.Biol Fertil Soils (2019) 55:765–766766

PuSH

Recommendations for soil microbiome analyses.

ARTS repository - University of Groningen

   University of GroningenRecommendations for soil microbiome analysesNannipieri, P.; Penton, C. R.; Purahong, W.; Schloter, M.; van Elsas, J. D.Published in:Biology and Fertility of SoilsDOI:10.1007/s00374-019-01409-zIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2019Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Nannipieri, P., Penton, C. R., Purahong, W., Schloter, M., & van Elsas, J. D. (2019). Recommendations forsoil microbiome analyses. Biology and Fertility of Soils , 55(8), 765-766. https://doi.org/10.1007/s00374-019-01409-zCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 07-06-2022EDITORIALRecommendations for soil microbiome analysesP. Nannipieri1 & C. R. Penton2 & W. Purahong3 & M. Schloter4 & J. D. van Elsas5# Springer-Verlag GmbH Germany, part of Springer Nature 2019As a result of the recent developments in high-throughputmolecular biology methods, our understanding of the soilmicrobiome has increased significantly over the last decades.Using microbiome DNA- or RNA-based analyses, such asamplicon sequencing and shotgun metagenomics, a largenumber of new phyla could be identified and some could beassigned to important traits, which drive keystone functionsfor ecosystem services provided by soils. However, there isstill a lack of information with respect to the major drivers thattrigger microbial community composition and function on allscales, i.e., from the pedon (microhabitat, at the micrometerscale) to the region. A large number of publications that in-cluded microbiome analyses have been submitted in the lastyear’s Biology and Fertility of Soils, and some have addressedsuch questions. However, in many cases, the provided infor-mation, despite the studies being sound in analytics, did notstrongly contribute to our understanding of the impact on, aswell as the function of, the soil microbiome for severalreasons.1) As a result of the high costs for analyses and the complexbioinformatics analysis, only one sampling time point isoften chosen for analysis, although there is a large body ofevidence on the high temporal dynamics of the soilmicrobiome, in particular when activity hotspots like therhizosphere, the mycosphere, or the detritusphere are con-sidered. Changes over time can be higher in magnitudethan those observed between treatments at a single sam-pling point, for instance due to differences in manage-ment, plant cover, and other variables (Nannipieri et al.2019). In many cases, samples from agricultural soils aretaken at crop harvest time. Thus, such studies mostly de-scribe the response of the soil microbiome to the distur-bance (and stress) caused by the harvest instead ofdepicting the typical response pattern and dynamics ofthe soil microbiome. It is deemed essential to select atleast two sampling time points, with at least one incurringa minimal disturbance (mostly in spring), using this as areference for the analysis of management-related factorslike fertilization, tillage, or cropping sequence.2) Besides the temporal dynamics of soil microbiomes, thereis also a huge heterogeneity in space, and so intricatestatistical techniques are required to grasp this variation.However, the multiple soil samples taken from individualfield plots are often composited to yield just one samplecovering a plot. In more elaborate studies, spatial hetero-geneity should be captured in order to statistically com-pare variances with plot and treatment differences, usingan appropriate number of replicates. Experimental de-signs with low number of replicates (e.g., three) reducethe statistical power of both univariate and multivariatestatistics.3) Linking microbial community composition or potentialfunction to the transformation processes that occur inthe soil is often performed only taking into account theconcentration of the product/substrate of the target pro-cess, ignoring the fact that the levels of these compoundsin the soil may also be dependent on other biotic/abioticprocesses. For example, changes in the concentration ofexchangeable ammonium or nitrate depend not only onthe activity of autotrophic nitrification but also on theactivity of other N cycling processes. Therefore, targetprocess rates should be determined to establish such link-ages and the quantification and/or presence/absence offunctional guilds should be included in the discussion.* P. Nannipieripaolo.nannipieri@unifi.it1 Department of Agriculture, Food, Environment and Forestry,University of Firenze, Florence, Italy2 College of Integrative Sciences and Arts, Arizona State University,Mesa, AZ, USA3 Department of Soil Ecology, UFZ-Helmholtz Centre forEnvironmental Research, Halle (Saale), Germany4 Research Unit for Comparative Microbiome Analysis, HelmholtzCenter for Environment and Health, Oberschleissheim, Germany5 Department of Microbial Ecology, GELIFES, University ofGroningen, Groningen, Netherlandshttps://doi.org/10.1007/s00374-019-01409-zBiology and Fertility of Soils (2019) 55:765–766Published online: 30            October     2   0194) The detection or quantification of a target gene is oftendirectly linked to a process in which the predicted proteinis presumed to be a central catalyst. However, it is impor-tant to consider and discuss that such a presumption isrisky, as the predicted protein may or may not be activeand be a central player within the microbial community.As a second confounding factor, using microbiome DNAas a proxy for potential function is often biased due to thepresence of DNA from dead microbes.5) Data generated by amplicon sequencing (coined by someauthors as “barcoding” or “metabarcoding”) and shotgunmetagenomics constitute “relative” data. Such data, bydefinition, do not provide any information on the totalabundances of the identified clades. Thus, mainly in soilsystems in which changes of microbial biomass occur,including for key organisms or functions, ribosomalRNA gene–based qPCR analyses are needed to defineabsolute abundances, which could serve as proxies forpotential transformation rates.6) The Earth Microbiome project (www.earthmicrobiome.org) has provided a number of important standards forthe analysis of soil microbiomes, which are very helpfulif the data from single projects are to be compared withstudies of relevance from other researchers in a meta-analysis. If no clear arguments exist against thesestandards, they should be implemented into allanalytical pipelines as soon as possible.We strongly recommend that the aforementioned six issuesare implemented in all future manuscripts submitted toBiology and Fertility of Soils, as has been advocated andsummarized in two recent papers (Schöler et al. 2017;Vestergaard et al. 2017).ReferencesNannipieri P, Ascher-Jenull J, Ceccherini MT, Pietramellara G, RenellaG, Schloter M (2019) Beyond microbial diversity for predicting soilfunctions. Pedosphere 29, in pressSchöler A, Jacquiod S, Vestergaard G, Schulz S, Schloter M (2017)Analysis of soil microbial communities based on amplicon sequenc-ing of marker genes. Biol Fertil Soils 53:485–489Vestergaard G, Schulz S, Schöler A, Schloter M (2017) Making big datasmart - how to use metagenomics to understand soil quality. BiolFertil Soils 53:479–484Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.Biol Fertil Soils (2019) 55:765–766766

   University of GroningenRecommendations for soil microbiome analysesNannipieri, P.; Penton, C. R.; Purahong, W.; Schloter, M.; van Elsas, J. D.Published in:Biology and Fertility of SoilsDOI:10.1007/s00374-019-01409-zIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2019Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Nannipieri, P., Penton, C. R., Purahong, W., Schloter, M., & van Elsas, J. D. (2019). Recommendations forsoil microbiome analyses. Biology and Fertility of Soils , 55(8), 765-766. https://doi.org/10.1007/s00374-019-01409-zCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 10-09-2023EDITORIALRecommendations for soil microbiome analysesP. Nannipieri1 & C. R. Penton2 & W. Purahong3 & M. Schloter4 & J. D. van Elsas5# Springer-Verlag GmbH Germany, part of Springer Nature 2019As a result of the recent developments in high-throughputmolecular biology methods, our understanding of the soilmicrobiome has increased significantly over the last decades.Using microbiome DNA- or RNA-based analyses, such asamplicon sequencing and shotgun metagenomics, a largenumber of new phyla could be identified and some could beassigned to important traits, which drive keystone functionsfor ecosystem services provided by soils. However, there isstill a lack of information with respect to the major drivers thattrigger microbial community composition and function on allscales, i.e., from the pedon (microhabitat, at the micrometerscale) to the region. A large number of publications that in-cluded microbiome analyses have been submitted in the lastyear’s Biology and Fertility of Soils, and some have addressedsuch questions. However, in many cases, the provided infor-mation, despite the studies being sound in analytics, did notstrongly contribute to our understanding of the impact on, aswell as the function of, the soil microbiome for severalreasons.1) As a result of the high costs for analyses and the complexbioinformatics analysis, only one sampling time point isoften chosen for analysis, although there is a large body ofevidence on the high temporal dynamics of the soilmicrobiome, in particular when activity hotspots like therhizosphere, the mycosphere, or the detritusphere are con-sidered. Changes over time can be higher in magnitudethan those observed between treatments at a single sam-pling point, for instance due to differences in manage-ment, plant cover, and other variables (Nannipieri et al.2019). In many cases, samples from agricultural soils aretaken at crop harvest time. Thus, such studies mostly de-scribe the response of the soil microbiome to the distur-bance (and stress) caused by the harvest instead ofdepicting the typical response pattern and dynamics ofthe soil microbiome. It is deemed essential to select atleast two sampling time points, with at least one incurringa minimal disturbance (mostly in spring), using this as areference for the analysis of management-related factorslike fertilization, tillage, or cropping sequence.2) Besides the temporal dynamics of soil microbiomes, thereis also a huge heterogeneity in space, and so intricatestatistical techniques are required to grasp this variation.However, the multiple soil samples taken from individualfield plots are often composited to yield just one samplecovering a plot. In more elaborate studies, spatial hetero-geneity should be captured in order to statistically com-pare variances with plot and treatment differences, usingan appropriate number of replicates. Experimental de-signs with low number of replicates (e.g., three) reducethe statistical power of both univariate and multivariatestatistics.3) Linking microbial community composition or potentialfunction to the transformation processes that occur inthe soil is often performed only taking into account theconcentration of the product/substrate of the target pro-cess, ignoring the fact that the levels of these compoundsin the soil may also be dependent on other biotic/abioticprocesses. For example, changes in the concentration ofexchangeable ammonium or nitrate depend not only onthe activity of autotrophic nitrification but also on theactivity of other N cycling processes. Therefore, targetprocess rates should be determined to establish such link-ages and the quantification and/or presence/absence offunctional guilds should be included in the discussion.* P. Nannipieripaolo.nannipieri@unifi.it1 Department of Agriculture, Food, Environment and Forestry,University of Firenze, Florence, Italy2 College of Integrative Sciences and Arts, Arizona State University,Mesa, AZ, USA3 Department of Soil Ecology, UFZ-Helmholtz Centre forEnvironmental Research, Halle (Saale), Germany4 Research Unit for Comparative Microbiome Analysis, HelmholtzCenter for Environment and Health, Oberschleissheim, Germany5 Department of Microbial Ecology, GELIFES, University ofGroningen, Groningen, Netherlandshttps://doi.org/10.1007/s00374-019-01409-zBiology and Fertility of Soils (2019) 55:765–766Published online: 30            October     2   0194) The detection or quantification of a target gene is oftendirectly linked to a process in which the predicted proteinis presumed to be a central catalyst. However, it is impor-tant to consider and discuss that such a presumption isrisky, as the predicted protein may or may not be activeand be a central player within the microbial community.As a second confounding factor, using microbiome DNAas a proxy for potential function is often biased due to thepresence of DNA from dead microbes.5) Data generated by amplicon sequencing (coined by someauthors as “barcoding” or “metabarcoding”) and shotgunmetagenomics constitute “relative” data. Such data, bydefinition, do not provide any information on the totalabundances of the identified clades. Thus, mainly in soilsystems in which changes of microbial biomass occur,including for key organisms or functions, ribosomalRNA gene–based qPCR analyses are needed to defineabsolute abundances, which could serve as proxies forpotential transformation rates.6) The Earth Microbiome project (www.earthmicrobiome.org) has provided a number of important standards forthe analysis of soil microbiomes, which are very helpfulif the data from single projects are to be compared withstudies of relevance from other researchers in a meta-analysis. If no clear arguments exist against thesestandards, they should be implemented into allanalytical pipelines as soon as possible.We strongly recommend that the aforementioned six issuesare implemented in all future manuscripts submitted toBiology and Fertility of Soils, as has been advocated andsummarized in two recent papers (Schöler et al. 2017;Vestergaard et al. 2017).ReferencesNannipieri P, Ascher-Jenull J, Ceccherini MT, Pietramellara G, RenellaG, Schloter M (2019) Beyond microbial diversity for predicting soilfunctions. Pedosphere 29, in pressSchöler A, Jacquiod S, Vestergaard G, Schulz S, Schloter M (2017)Analysis of soil microbial communities based on amplicon sequenc-ing of marker genes. Biol Fertil Soils 53:485–489Vestergaard G, Schulz S, Schöler A, Schloter M (2017) Making big datasmart - how to use metagenomics to understand soil quality. BiolFertil Soils 53:479–484Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.Biol Fertil Soils (2019) 55:765–766766

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Recommendations for soil microbiome analyses.

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PuSH

ARTS repository - University of Groningen

ARTS repository - University of Groningen