The evolution of trait correlations constrains phenotypic adaptation to high CO2 in a eukaryotic alga

Walworth, NG; Hinners, J; Argyle, PA; Leles, SG; Doblin, MA; Collins, S; Levine, NM

The evolution of trait correlations constrains phenotypic adaptation to high CO2 in a eukaryotic alga

Walworth, NG Hinners, J Argyle, PA Leles, SG Doblin, MA Collins, S Levine, NM

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Publisher:: The Royal Society
Publication Type:: Journal Article
Citation:: Proceedings of the Royal Society B: Biological Sciences, 2021, 282, (1953), pp. 1-9
Issue Date:: 2021-06-16

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Field	Value	Language
dc.contributor.author	Walworth, NG
dc.contributor.author	Hinners, J
dc.contributor.author	Argyle, PA
dc.contributor.author	Leles, SG
dc.contributor.author	Doblin, MA
dc.contributor.author	Collins, S
dc.contributor.author	Levine, NM
dc.date.accessioned	2022-02-15T03:33:16Z
dc.date.available	2022-02-15T03:33:16Z
dc.date.issued	2021-06-16
dc.identifier.citation	Proceedings of the Royal Society B: Biological Sciences, 2021, 282, (1953), pp. 1-9
dc.identifier.issn	0962-8452
dc.identifier.issn	1471-2954
dc.identifier.uri	http://hdl.handle.net/10453/154525
dc.description.abstract	Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO2. We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	The Royal Society
dc.relation.ispartof	Proceedings of the Royal Society B: Biological Sciences
dc.relation.isbasedon	10.1098/rspb.2021.0940
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	06 Biological Sciences, 07 Agricultural and Veterinary Sciences, 11 Medical and Health Sciences
dc.subject.mesh	Adaptation, Physiological
dc.subject.mesh	Biological Evolution
dc.subject.mesh	Carbon Dioxide
dc.subject.mesh	Eukaryota
dc.subject.mesh	Phenotype
dc.subject.mesh	Carbon Dioxide
dc.subject.mesh	Adaptation, Physiological
dc.subject.mesh	Phenotype
dc.subject.mesh	Eukaryota
dc.subject.mesh	Biological Evolution
dc.title	The evolution of trait correlations constrains phenotypic adaptation to high CO2 in a eukaryotic alga
dc.type	Journal Article
utslib.citation.volume	282
utslib.location.activity	England
utslib.for	06 Biological Sciences
utslib.for	07 Agricultural and Veterinary Sciences
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - C3 - Climate Change Cluster
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-15T03:33:07Z
pubs.issue	1953
pubs.publication-status	Published
pubs.volume	282
utslib.citation.issue	1953

Abstract:

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO2. We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.

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http://hdl.handle.net/10453/154525