Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address...

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Vydané v:	Frontiers in microbiology Ročník 7; číslo C; s. 214
Hlavní autori:	Graham, Emily B., Knelman, Joseph E., Schindlbacher, Andreas, Siciliano, Steven, Breulmann, Marc, Yannarell, Anthony, Beman, J. M., Abell, Guy, Philippot, Laurent, Prosser, James, Foulquier, Arnaud, Yuste, Jorge C., Glanville, Helen C., Jones, Davey L., Angel, Roey, Salminen, Janne, Newton, Ryan J., Bürgmann, Helmut, Ingram, Lachlan J., Hamer, Ute, Siljanen, Henri M. P., Peltoniemi, Krista, Potthast, Karin, Bañeras, Lluís, Hartmann, Martin, Banerjee, Samiran, Yu, Ri-Qing, Nogaro, Geraldine, Richter, Andreas, Koranda, Marianne, Castle, Sarah C., Goberna, Marta, Song, Bongkeun, Chatterjee, Amitava, Nunes, Olga C., Lopes, Ana R., Cao, Yiping, Kaisermann, Aurore, Hallin, Sara, Strickland, Michael S., Garcia-Pausas, Jordi, Barba, Josep, Kang, Hojeong, Isobe, Kazuo, Papaspyrou, Sokratis, Pastorelli, Roberta, Lagomarsino, Alessandra, Lindström, Eva S., Basiliko, Nathan, Nemergut, Diana R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Media 24.02.2016 Frontiers Research Foundation Frontiers Media S.A
Predmet:	BASIC BIOLOGICAL SCIENCES Carbon Cycle Denitrification Ecologia microbiana Ecology ecosystem processes Ekologi ENVIRONMENTAL SCIENCES functional gene microbial diversity Microbial ecology Microbiology Mikrobiologi Nitrification Nitrogen Cycle Respiration statistical modeling functional gene nitrification ecosystem processes statistical modeling respiration microbial diversity microbial ecology denitrification
ISSN:	1664-302X, 1664-302X
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Abstract	Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.
AbstractList	Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology. In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology. Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.
Author	Hartmann, Martin Pastorelli, Roberta Foulquier, Arnaud Richter, Andreas Hallin, Sara Barba, Josep Siciliano, Steven Bürgmann, Helmut Peltoniemi, Krista Basiliko, Nathan Yuste, Jorge C. Newton, Ryan J. Bañeras, Lluís Schindlbacher, Andreas Knelman, Joseph E. Kaisermann, Aurore Nogaro, Geraldine Beman, J. M. Isobe, Kazuo Lopes, Ana R. Prosser, James Banerjee, Samiran Potthast, Karin Papaspyrou, Sokratis Ingram, Lachlan J. Nunes, Olga C. Jones, Davey L. Chatterjee, Amitava Nemergut, Diana R. Castle, Sarah C. Lagomarsino, Alessandra Hamer, Ute Siljanen, Henri M. P. Kang, Hojeong Abell, Guy Yannarell, Anthony Koranda, Marianne Breulmann, Marc Strickland, Michael S. Garcia-Pausas, Jordi Graham, Emily B. Goberna, Marta Philippot, Laurent Lindström, Eva S. Glanville, Helen C. Yu, Ri-Qing Song, Bongkeun Cao, Yiping Angel, Roey Salminen, Janne
AuthorAffiliation	24 Institute of Aquatic Ecology, Facultat de Ciències, University of Girona Girona, Spain 2 Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, USA 19 Centre for Carbon, Water and Food, The University of Sydney, Sydney NSW, Australia 21 Department of Environmental and Biological Sciences, University of Eastern Finland Kuopio, Finland 45 Research Centre for Agrobiology and Pedology Florence, Italy 15 Department of Microbiology and Ecosystem Science, University of Vienna Vienna, Austria 18 Department of Surface Waters, Eawag: Swiss Federal Institute of Aquatic Science and Technology Kastanienbaum, Switzerland 31 Department of Ecosystem and Conservation Sciences, University of Montana, Missoula MT, USA 28 EDF R&D, National Hydraulics and Environmental Laboratory Chatou, France 35 LEPABE - Laboratory for Process Engineering, Environmental, Biotechnology and Energy, Faculdade de Engenharia da Universidade do Porto Porto, Portugal 47 Vale Living with Lakes Centre and Depa
AuthorAffiliation_xml	– name: 43 Department of Applied Biological Chemistry, The University of Tokyo Tokyo, Japan – name: 4 Department of Forest Ecology, Federal Research and Training Centre for Forests, Bundesforschungs- und Ausbildungszentrum für Wald Vienna, Austria – name: 16 Häme University of Applied Sciences Hämeenlinna, Finland – name: 13 Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones Científicas Madrid, Spain – name: 3 US Department of Energy, Joint Genome Institute, Walnut Creek CA, USA – name: 21 Department of Environmental and Biological Sciences, University of Eastern Finland Kuopio, Finland – name: 26 CSIRO Agriculture Flagship, Crace ACT, Australia – name: 12 Irstea, UR MALY, Centre de Lyon-Villeurbanne Villeurbanne, France – name: 38 Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences Uppsala, Sweden – name: 27 Department of Biology, University of Texas at Tyler, Tyler TX, USA – name: 29 Department of Microbiology and Ecosystem Science, University of Vienna Vienna, Austria – name: 6 Helmholtz Centre for Environmental Research – Centre for Environmental Biotechnology Leipzig, Germany – name: 42 School of Civil and Environmental Engineering, Yonsei University Seoul, South Korea – name: 20 Institute of Landscape Ecology, University of Münster Münster, Germany – name: 34 AES School of Natural Resources Sciences, North Dakota State University, Fargo ND, USA – name: 8 Life and Environmental Sciences and Sierra Nevada Research Institute, University of California – Merced, Merced CA, USA – name: 40 Centre Tecnològic Forestal de Catalunya Solsona, Spain – name: 35 LEPABE - Laboratory for Process Engineering, Environmental, Biotechnology and Energy, Faculdade de Engenharia da Universidade do Porto Porto, Portugal – name: 41 Centre de Recerca Ecològica i Aplicacions Forestals, Cerdanyola del Vallès Barcelona, Spain – name: 1 Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder CO, USA – name: 2 Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, USA – name: 47 Vale Living with Lakes Centre and Department of Biology, Laurentian University, Sudbury ON, Canada – name: 7 Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana IL, USA – name: 44 Department of Biomedicine, Biotechnology and Public Health, University of Cadiz Puerto Real, Spain – name: 19 Centre for Carbon, Water and Food, The University of Sydney, Sydney NSW, Australia – name: 45 Research Centre for Agrobiology and Pedology Florence, Italy – name: 48 Biology Department, Duke University, Durham NC, USA – name: 17 School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee WI, USA – name: 32 Centro de Investigación y Docencia Económicas – Consejo Superior de Investigaciones Científicas Valencia, Spain – name: 18 Department of Surface Waters, Eawag: Swiss Federal Institute of Aquatic Science and Technology Kastanienbaum, Switzerland – name: 39 Department of Biological Sciences, Virginia Polytechnic Institute, State University, Blacksburg VA, USA – name: 36 Southern California Coastal Water Research Project Authority, Costa Mesa CA, USA – name: 10 Institut National de la Recherche Agronomique – Agroecology Dijon, France – name: 28 EDF R&D, National Hydraulics and Environmental Laboratory Chatou, France – name: 31 Department of Ecosystem and Conservation Sciences, University of Montana, Missoula MT, USA – name: 14 Environment Centre Wales, Bangor University Gwynedd, UK – name: 33 Department of Biological Science, Virginia Institute of Marine Science, Gloucester Point VA, USA – name: 46 Department of Ecology and Genetics/Limnology, Uppsala University Uppsala, Sweden – name: 25 Institute for Sustainability Sciences – Agroscope Zurich, Switzerland – name: 5 Department of Soil Science, University of Saskatchewan, Saskatoon SK, Canada – name: 23 Institute of Soil Science and Site Ecology, Technische University Dresden, Germany – name: 30 Division of Terrestrial Ecosystem Research, Department of Microbiology and Ecosystem Science, University of Vienna Vienna, Austria – name: 15 Department of Microbiology and Ecosystem Science, University of Vienna Vienna, Austria – name: 22 Natural Resources Institute Vantaa, Finland – name: 9 School of Medicine, Flinders University, Adelaide SA, Australia – name: 24 Institute of Aquatic Ecology, Facultat de Ciències, University of Girona Girona, Spain – name: 37 UMR, Interactions Sol Plante Atmosphère, INRA Bordeaux Villenave d’Ornon, France – name: 11 Institute of Biological and Environmental Sciences, University of Aberdeen Aberdeen, UK
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Copyright	Attribution 4.0 Spain info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/es Copyright © 2016 Graham, Knelman, Schindlbacher, Siciliano, Breulmann, Yannarell, Beman, Abell, Philippot, Prosser, Foulquier, Yuste, Glanville, Jones, Angel, Salminen, Newton, Bürgmann, Ingram, Hamer, Siljanen, Peltoniemi, Potthast, Bañeras, Hartmann, Banerjee, Yu, Nogaro, Richter, Koranda, Castle, Goberna, Song, Chatterjee, Nunes, Lopes, Cao, Kaisermann, Hallin, Strickland, Garcia-Pausas, Barba, Kang, Isobe, Papaspyrou, Pastorelli, Lagomarsino, Lindström, Basiliko and Nemergut. 2016 Graham, Knelman, Schindlbacher, Siciliano, Breulmann, Yannarell, Beman, Abell, Philippot, Prosser, Foulquier, Yuste, Glanville, Jones, Angel, Salminen, Newton, Bürgmann, Ingram, Hamer, Siljanen, Peltoniemi, Potthast, Bañeras, Hartmann, Banerjee, Yu, Nogaro, Richter, Koranda, Castle, Goberna, Song, Chatterjee, Nunes, Lopes, Cao, Kaisermann, Hallin, Strickland, Garcia-Pausas, Barba, Kang, Isobe, Papaspyrou, Pastorelli, Lagomarsino, Lindström, Basiliko and Nemergut
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Keywords	functional gene nitrification ecosystem processes statistical modeling respiration microbial diversity microbial ecology denitrification
Language	English
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 USDOE DEB1221215; AC0576RL01830 Edited by: Gary M. King, Louisiana State University, USA Reviewed by: Steffen Kolb, Landscape Biogeochemistry – Leibniz Centre for Agricultural Landscape Research, Germany; Hongchen Jiang, Miami University, USA; Kristen M. DeAngelis, University of Massachusetts Amherst, USA This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology
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Snippet	Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial... Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial... In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex...
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SubjectTerms	BASIC BIOLOGICAL SCIENCES Carbon Cycle Denitrification Ecologia microbiana Ecology ecosystem processes Ekologi ENVIRONMENTAL SCIENCES functional gene microbial diversity Microbial ecology Microbiology Mikrobiologi Nitrification Nitrogen Cycle Respiration statistical modeling
Title	Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?
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