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dc.creatorWidder, S
dc.creatorAllen, RJ
dc.creatorPfeiffer, T
dc.creatorCurtis, TP
dc.creatorWiuf, C
dc.creatorSloan, WT
dc.creatorCordero, OX
dc.creatorBrown, SP
dc.creatorMomeni, B
dc.creatorShou, W
dc.creatorKettle, H
dc.creatorFlint, HJ
dc.creatorHaas, AF
dc.creatorLaroche, B
dc.creatorKreft, JU
dc.creatorRainey, PB
dc.creatorFreilich, S
dc.creatorSchuster, S
dc.creatorMilferstedt, K
dc.creatorVan Der Meer, JR
dc.creatorGrobkopf, T
dc.creatorHuisman, J
dc.creatorFree, A
dc.creatorPicioreanu, C
dc.creatorQuince, C
dc.creatorKlapper, I
dc.creatorLabarthe, S
dc.creatorSmets, BF
dc.creatorWang, H
dc.creatorSoyer, OS
dc.creatorAllison, SD
dc.creatorChong, J
dc.creatorLagomarsino, MC
dc.creatorCroze, OA
dc.creatorHamelin, J
dc.creatorHarmand, J
dc.creatorHoyle, R
dc.creatorHwa, TT
dc.creatorJin, Q
dc.creatorJohnson, DR
dc.creatorde Lorenzo, V
dc.creatorMobilia, M
dc.creatorMurphy, B
dc.creatorPeaudecerf, F
dc.creatorProsser, JI
dc.creatorQuinn, RA
dc.creatorRalser, M
dc.creatorSmith, AG
dc.creatorSteyer, JP
dc.creatorSwainston, N
dc.creatorTarnita, CE
dc.creatorTrably, E
dc.creatorWarren, PB
dc.creatorWilmes, P
dc.date.accessioned2021-01-27T21:58:51Z
dc.date.available2021-01-27T21:58:51Z
dc.date.issued2016-11-01
dc.identifier.issn1751-7362
dc.identifier.issn1751-7370
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5051
dc.identifier.other27022995 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5069
dc.description.abstract© 2016 International Society for Microbial Ecology All rights reserved. The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.
dc.format.extent2557-2568
dc.language.isoen
dc.relation.haspartISME Journal
dc.relation.isreferencedbySpringer Science and Business Media LLC
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAir Microbiology
dc.subjectAnimals
dc.subjectEcosystem
dc.subjectHumans
dc.subjectModels, Theoretical
dc.subjectSeawater
dc.subjectSoil Microbiology
dc.titleChallenges in microbial ecology: Building predictive understanding of community function and dynamics
dc.typeArticle
dc.type.genreReview
dc.type.genreJournal
dc.relation.doi10.1038/ismej.2016.45
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-27T21:58:46Z
refterms.dateFOA2021-01-27T21:58:51Z


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