The evolution of trait correlations constrains phenotypic adaptation to high CO 2 in a eukaryotic alga

Nathan G. Walworth, Jana Hinners, Phoebe A. Argyle, Suzana G. Leles, Martina A. Doblin, Sinéad Collins, Naomi M. Levine

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Article number20210940
Number of pages9
JournalProceedings of the Royal Society B: Biological Sciences
Issue number1953
Publication statusPublished - 30 Jun 2021

Keywords / Materials (for Non-textual outputs)

  • microbial evolution
  • trait correlations
  • trait adaptation
  • phytoplankton
  • biogeochemistry
  • principal component analyses


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