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dc.contributor.authorGraham, Emily B
dc.contributor.authorKnelman, Joseph E.
dc.contributor.authorSchindlbacher, Andreas
dc.contributor.authorSiciliano, Steven
dc.contributor.authorBreulmann, Marc
dc.contributor.authorYannarell, Anothony
dc.contributor.authorBeman, J M
dc.contributor.authorAbell, Guy
dc.contributor.authorPhilippot, Laurent
dc.contributor.authorProsser, James
dc.contributor.authorFoulquier, Arnaud
dc.contributor.authorYuste, Jorge C
dc.contributor.authorGlanville, Helen C.
dc.contributor.authorJones, Davey L.
dc.contributor.authorAngel, Roey
dc.contributor.authorSalminen, Janne
dc.contributor.authorNewton, Ryan J
dc.contributor.authorBurgmann, Helmut
dc.contributor.authorIngram, Lachlan J
dc.contributor.authorHamer, Ute
dc.contributor.authorSiljanen, Henri M P
dc.contributor.authorPeltoniemi, Krista
dc.contributor.authorPotthast, Karin
dc.contributor.authorBaneras, Lluis
dc.contributor.authorHartmann, Martin
dc.contributor.authorBanerjee, Samiran
dc.contributor.authorYu, Ri-Qing
dc.contributor.authorNogaro, Geraldine
dc.contributor.authorRichter, Andreas
dc.contributor.authorKoranda, Marianne
dc.contributor.authorCastle, Sarah C
dc.contributor.authorGoberna, Marta
dc.contributor.authorSong, Bongkeun
dc.contributor.authorChaterjee, Amitava
dc.contributor.authorNunes, Olgo C
dc.contributor.authorLopes, Ana R
dc.contributor.authorCao, Yiping
dc.contributor.authorKaisermann, Aurore
dc.contributor.authorHallin, Sara
dc.contributor.authorStickland, Michael S
dc.contributor.authorGarcia-Pausas, Jordi
dc.contributor.authorBarba, Joseph
dc.contributor.authorKang, Hojeong
dc.contributor.authorIsobe, Kazuo
dc.contributor.authorPapaspyrou, Sokratis
dc.contributor.authorPastorelli, Roberta
dc.contributor.authorLagomarsino, Alessandra
dc.contributor.authorLindstrom, Eva S
dc.contributor.authorBasiliko, Nathan
dc.contributor.authorNemergut, Diana R
dc.date.accessioned2016-10-25T23:36:11Z
dc.date.available2016-10-25T23:36:11Z
dc.date.issued2016
dc.identifier.citationGraham EB, Knelman JE, Schindlbacher A, et al. Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes? Frontiers in Microbiology. 2016;7:214. doi:10.3389/fmicb.2016.00214.en
dc.identifier.issn1664-302X
dc.identifier.urihttp://hdl.handle.net/2328/36473
dc.descriptionThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.description.abstractMicroorganisms 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.en
dc.language.isoen
dc.publisherFrontiers Mediaen
dc.rightsCopyright © 2016 The Authorsen
dc.titleMicrobes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?en
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.3389/fmicb.2016.00214en
dc.rights.holderThe Authorsen


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