Understanding microbial community dynamics to improve optimal microbiome selection

Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and...
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Autor*in:	Wright, Robyn J. [verfasserIn] Gibson, Matthew I. [verfasserIn] Christie-Oleza, Joseph A. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Artificial microbiome selection Microbial communities Microbial ecology Polymer degradation Chitin degradation Ecological succession Microbial community dynamics

Übergeordnetes Werk:	Enthalten in: Microbiome - London : Biomed Central, 2013, 7(2019), 1 vom: 03. Juni
Übergeordnetes Werk:	volume:7 ; year:2019 ; number:1 ; day:03 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s40168-019-0702-x

Katalog-ID:	SPR033285098

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520			\|a Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies.
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allfields	10.1186/s40168-019-0702-x doi (DE-627)SPR033285098 (SPR)s40168-019-0702-x-e DE-627 ger DE-627 rakwb eng 570 ASE Wright, Robyn J. verfasserin aut Understanding microbial community dynamics to improve optimal microbiome selection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies. Artificial microbiome selection (dpeaa)DE-He213 Microbial communities (dpeaa)DE-He213 Microbial ecology (dpeaa)DE-He213 Polymer degradation (dpeaa)DE-He213 Chitin degradation (dpeaa)DE-He213 Ecological succession (dpeaa)DE-He213 Microbial community dynamics (dpeaa)DE-He213 Gibson, Matthew I. verfasserin aut Christie-Oleza, Joseph A. verfasserin aut Enthalten in Microbiome London : Biomed Central, 2013 7(2019), 1 vom: 03. Juni (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:7 year:2019 number:1 day:03 month:06 https://dx.doi.org/10.1186/s40168-019-0702-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 1 03 06
spelling	10.1186/s40168-019-0702-x doi (DE-627)SPR033285098 (SPR)s40168-019-0702-x-e DE-627 ger DE-627 rakwb eng 570 ASE Wright, Robyn J. verfasserin aut Understanding microbial community dynamics to improve optimal microbiome selection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies. Artificial microbiome selection (dpeaa)DE-He213 Microbial communities (dpeaa)DE-He213 Microbial ecology (dpeaa)DE-He213 Polymer degradation (dpeaa)DE-He213 Chitin degradation (dpeaa)DE-He213 Ecological succession (dpeaa)DE-He213 Microbial community dynamics (dpeaa)DE-He213 Gibson, Matthew I. verfasserin aut Christie-Oleza, Joseph A. verfasserin aut Enthalten in Microbiome London : Biomed Central, 2013 7(2019), 1 vom: 03. Juni (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:7 year:2019 number:1 day:03 month:06 https://dx.doi.org/10.1186/s40168-019-0702-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 1 03 06
allfields_unstemmed	10.1186/s40168-019-0702-x doi (DE-627)SPR033285098 (SPR)s40168-019-0702-x-e DE-627 ger DE-627 rakwb eng 570 ASE Wright, Robyn J. verfasserin aut Understanding microbial community dynamics to improve optimal microbiome selection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies. Artificial microbiome selection (dpeaa)DE-He213 Microbial communities (dpeaa)DE-He213 Microbial ecology (dpeaa)DE-He213 Polymer degradation (dpeaa)DE-He213 Chitin degradation (dpeaa)DE-He213 Ecological succession (dpeaa)DE-He213 Microbial community dynamics (dpeaa)DE-He213 Gibson, Matthew I. verfasserin aut Christie-Oleza, Joseph A. verfasserin aut Enthalten in Microbiome London : Biomed Central, 2013 7(2019), 1 vom: 03. Juni (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:7 year:2019 number:1 day:03 month:06 https://dx.doi.org/10.1186/s40168-019-0702-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 1 03 06
allfieldsGer	10.1186/s40168-019-0702-x doi (DE-627)SPR033285098 (SPR)s40168-019-0702-x-e DE-627 ger DE-627 rakwb eng 570 ASE Wright, Robyn J. verfasserin aut Understanding microbial community dynamics to improve optimal microbiome selection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies. Artificial microbiome selection (dpeaa)DE-He213 Microbial communities (dpeaa)DE-He213 Microbial ecology (dpeaa)DE-He213 Polymer degradation (dpeaa)DE-He213 Chitin degradation (dpeaa)DE-He213 Ecological succession (dpeaa)DE-He213 Microbial community dynamics (dpeaa)DE-He213 Gibson, Matthew I. verfasserin aut Christie-Oleza, Joseph A. verfasserin aut Enthalten in Microbiome London : Biomed Central, 2013 7(2019), 1 vom: 03. Juni (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:7 year:2019 number:1 day:03 month:06 https://dx.doi.org/10.1186/s40168-019-0702-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 1 03 06
allfieldsSound	10.1186/s40168-019-0702-x doi (DE-627)SPR033285098 (SPR)s40168-019-0702-x-e DE-627 ger DE-627 rakwb eng 570 ASE Wright, Robyn J. verfasserin aut Understanding microbial community dynamics to improve optimal microbiome selection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies. Artificial microbiome selection (dpeaa)DE-He213 Microbial communities (dpeaa)DE-He213 Microbial ecology (dpeaa)DE-He213 Polymer degradation (dpeaa)DE-He213 Chitin degradation (dpeaa)DE-He213 Ecological succession (dpeaa)DE-He213 Microbial community dynamics (dpeaa)DE-He213 Gibson, Matthew I. verfasserin aut Christie-Oleza, Joseph A. verfasserin aut Enthalten in Microbiome London : Biomed Central, 2013 7(2019), 1 vom: 03. Juni (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:7 year:2019 number:1 day:03 month:06 https://dx.doi.org/10.1186/s40168-019-0702-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 1 03 06
language	English
source	Enthalten in Microbiome 7(2019), 1 vom: 03. Juni volume:7 year:2019 number:1 day:03 month:06
sourceStr	Enthalten in Microbiome 7(2019), 1 vom: 03. Juni volume:7 year:2019 number:1 day:03 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial microbiome selection Microbial communities Microbial ecology Polymer degradation Chitin degradation Ecological succession Microbial community dynamics
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container_title	Microbiome
authorswithroles_txt_mv	Wright, Robyn J. @@aut@@ Gibson, Matthew I. @@aut@@ Christie-Oleza, Joseph A. @@aut@@
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Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. 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author	Wright, Robyn J.
spellingShingle	Wright, Robyn J. ddc 570 misc Artificial microbiome selection misc Microbial communities misc Microbial ecology misc Polymer degradation misc Chitin degradation misc Ecological succession misc Microbial community dynamics Understanding microbial community dynamics to improve optimal microbiome selection
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topic_title	570 ASE Understanding microbial community dynamics to improve optimal microbiome selection Artificial microbiome selection (dpeaa)DE-He213 Microbial communities (dpeaa)DE-He213 Microbial ecology (dpeaa)DE-He213 Polymer degradation (dpeaa)DE-He213 Chitin degradation (dpeaa)DE-He213 Ecological succession (dpeaa)DE-He213 Microbial community dynamics (dpeaa)DE-He213
topic	ddc 570 misc Artificial microbiome selection misc Microbial communities misc Microbial ecology misc Polymer degradation misc Chitin degradation misc Ecological succession misc Microbial community dynamics
topic_unstemmed	ddc 570 misc Artificial microbiome selection misc Microbial communities misc Microbial ecology misc Polymer degradation misc Chitin degradation misc Ecological succession misc Microbial community dynamics
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title	Understanding microbial community dynamics to improve optimal microbiome selection
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title_full	Understanding microbial community dynamics to improve optimal microbiome selection
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title_sort	understanding microbial community dynamics to improve optimal microbiome selection
title_auth	Understanding microbial community dynamics to improve optimal microbiome selection
abstract	Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies.
abstractGer	Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies.
abstract_unstemmed	Background Artificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown. Results Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective transfers was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms. Conclusions The selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies.
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title_short	Understanding microbial community dynamics to improve optimal microbiome selection
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author2	Gibson, Matthew I. Christie-Oleza, Joseph A.
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