Microbes as engines of ecosystem function

When does community structure enhance predictions of ecosystem processes?

Emily B. Graham*, Joseph E. Knelman, Andreas Schindlbacher, Steven Siciliano, Marc Breulmann, Anthony Yannarell, J. M. Beman, Guy Abell, Laurent Philippot, James Prosser, Arnaud Foulquier, Jorge C. Yuste, Helen C. Glanville, Davey L. Jones, Roey Angel, Janne Salminen, Ryan J. Newton, Helmut Bürgmann, Lachlan J. Ingram, Ute Hamer & 30 others Henri M. P. Siljanen, Krista Peltoniemi, Karin Potthast, Lluís Bañeras, Martin Hartmann, Samiran Banerjee, Ri-Qing Yu, Geraldine Nogaro, Andreas Richter, Marianne Koranda, Sarah C. Castle, Marta Goberna, Bongkeun Song, Amitava Chatterjee, Olga C. Nunes, Ana R. Lopes, Yiping Cao, Aurore Kaisermann, Sara Hallin, Michael S. Strickland, Jordi Garcia-Pausas, Josep Barba, Hojeong Kang, Kazuo Isobe, Sokratis Papaspyrou, Roberta Pastorelli, Alessandra Lagomarsino, Eva S. Lindström, Nathan Basiliko, Diana R. Nemergut

*Corresponding author for this work

Research output: Contribution to journalArticle

128 Citations (Scopus)
4 Downloads (Pure)

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.

Original languageEnglish
Article number214
Pages (from-to)1-10
Number of pages10
JournalFrontiers in Microbiology
Volume7
DOIs
Publication statusPublished - 24 Feb 2016

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Ecosystem
Biomass
Biota
Respiratory Rate
Ecology
Nitrogen
Carbon
Research
Genes
Datasets

Keywords

  • Denitrification
  • Ecosystem processes
  • Functional gene
  • Microbial diversity
  • Microbial ecology
  • Nitrification
  • Respiration
  • Statistical modeling

ASJC Scopus subject areas

  • Microbiology
  • Microbiology (medical)

Cite this

Graham, E. B., Knelman, J. E., Schindlbacher, A., Siciliano, S., Breulmann, M., Yannarell, A., ... Nemergut, D. R. (2016). Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes? Frontiers in Microbiology, 7, 1-10. [214]. https://doi.org/10.3389/fmicb.2016.00214

Microbes as engines of ecosystem function : When does community structure enhance predictions of ecosystem processes? / 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.

In: Frontiers in Microbiology, Vol. 7, 214, 24.02.2016, p. 1-10.

Research output: Contribution to journalArticle

Graham, EB, Knelman, JE, Schindlbacher, A, Siciliano, S, Breulmann, M, Yannarell, A, Beman, JM, Abell, G, Philippot, L, Prosser, J, Foulquier, A, Yuste, JC, Glanville, HC, Jones, DL, Angel, R, Salminen, J, Newton, RJ, Bürgmann, H, Ingram, LJ, Hamer, U, Siljanen, HMP, Peltoniemi, K, Potthast, K, Bañeras, L, Hartmann, M, Banerjee, S, Yu, R-Q, Nogaro, G, Richter, A, Koranda, M, Castle, SC, Goberna, M, Song, B, Chatterjee, A, Nunes, OC, Lopes, AR, Cao, Y, Kaisermann, A, Hallin, S, Strickland, MS, Garcia-Pausas, J, Barba, J, Kang, H, Isobe, K, Papaspyrou, S, Pastorelli, R, Lagomarsino, A, Lindström, ES, Basiliko, N & Nemergut, DR 2016, 'Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?', Frontiers in Microbiology, vol. 7, 214, pp. 1-10. https://doi.org/10.3389/fmicb.2016.00214
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. / Microbes as engines of ecosystem function : When does community structure enhance predictions of ecosystem processes?. In: Frontiers in Microbiology. 2016 ; Vol. 7. pp. 1-10.
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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.",
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author = "Graham, {Emily B.} and Knelman, {Joseph E.} and Andreas Schindlbacher and Steven Siciliano and Marc Breulmann and Anthony Yannarell and Beman, {J. M.} and Guy Abell and Laurent Philippot and James Prosser and Arnaud Foulquier and Yuste, {Jorge C.} and Glanville, {Helen C.} and Jones, {Davey L.} and Roey Angel and Janne Salminen and Newton, {Ryan J.} and Helmut B{\"u}rgmann and Ingram, {Lachlan J.} and Ute Hamer and Siljanen, {Henri M. P.} and Krista Peltoniemi and Karin Potthast and Llu{\'i}s Ba{\~n}eras and Martin Hartmann and Samiran Banerjee and Ri-Qing Yu and Geraldine Nogaro and Andreas Richter and Marianne Koranda and Castle, {Sarah C.} and Marta Goberna and Bongkeun Song and Amitava Chatterjee and Nunes, {Olga C.} and Lopes, {Ana R.} and Yiping Cao and Aurore Kaisermann and Sara Hallin and Strickland, {Michael S.} and Jordi Garcia-Pausas and Josep Barba and Hojeong Kang and Kazuo Isobe and Sokratis Papaspyrou and Roberta Pastorelli and Alessandra Lagomarsino and Lindstr{\"o}m, {Eva S.} and Nathan Basiliko and Nemergut, {Diana R.}",
note = "FUNDING This work was supported by NSF grant DEB-1221215 to DN, as well as grants supporting the generation of our datasets as acknowledged in their original publications and in Supplementary Table S1. ACKNOWLEDGMENT We thank the USGS Powell Center ‘Next Generation Microbes’ working group, anonymous reviews, Brett Melbourne, and Alan Townsend for valuable feedback on this project.",
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T2 - When does community structure enhance predictions of ecosystem processes?

AU - Graham, Emily B.

AU - Knelman, Joseph E.

AU - Schindlbacher, Andreas

AU - Siciliano, Steven

AU - Breulmann, Marc

AU - Yannarell, Anthony

AU - Beman, J. M.

AU - Abell, Guy

AU - Philippot, Laurent

AU - Prosser, James

AU - Foulquier, Arnaud

AU - Yuste, Jorge C.

AU - Glanville, Helen C.

AU - Jones, Davey L.

AU - Angel, Roey

AU - Salminen, Janne

AU - Newton, Ryan J.

AU - Bürgmann, Helmut

AU - Ingram, Lachlan J.

AU - Hamer, Ute

AU - Siljanen, Henri M. P.

AU - Peltoniemi, Krista

AU - Potthast, Karin

AU - Bañeras, Lluís

AU - Hartmann, Martin

AU - Banerjee, Samiran

AU - Yu, Ri-Qing

AU - Nogaro, Geraldine

AU - Richter, Andreas

AU - Koranda, Marianne

AU - Castle, Sarah C.

AU - Goberna, Marta

AU - Song, Bongkeun

AU - Chatterjee, Amitava

AU - Nunes, Olga C.

AU - Lopes, Ana R.

AU - Cao, Yiping

AU - Kaisermann, Aurore

AU - Hallin, Sara

AU - Strickland, Michael S.

AU - Garcia-Pausas, Jordi

AU - Barba, Josep

AU - Kang, Hojeong

AU - Isobe, Kazuo

AU - Papaspyrou, Sokratis

AU - Pastorelli, Roberta

AU - Lagomarsino, Alessandra

AU - Lindström, Eva S.

AU - Basiliko, Nathan

AU - Nemergut, Diana R.

N1 - FUNDING This work was supported by NSF grant DEB-1221215 to DN, as well as grants supporting the generation of our datasets as acknowledged in their original publications and in Supplementary Table S1. ACKNOWLEDGMENT We thank the USGS Powell Center ‘Next Generation Microbes’ working group, anonymous reviews, Brett Melbourne, and Alan Townsend for valuable feedback on this project.

PY - 2016/2/24

Y1 - 2016/2/24

N2 - 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.

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KW - Denitrification

KW - Ecosystem processes

KW - Functional gene

KW - Microbial diversity

KW - Microbial ecology

KW - Nitrification

KW - Respiration

KW - Statistical modeling

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DO - 10.3389/fmicb.2016.00214

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