Open AccessThe evolution of trait correlations constrains phenotypic adaptation to high CO2in a eukaryotic alga
Author(s) -
Nathan G. Walworth,
Jana Hinners,
Phoebe Argyle,
Suzana Gonçalves Leles,
Martina A. Doblin,
Sinéad Collins,
Naomi M. Levine
Publication year - 2021
Publication title -
proceedings - royal society. biological sciences/proceedings - royal society. biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.342
H-Index - 253
eISSN - 1471-2954
pISSN - 0962-8452
DOI - 10.1098/rspb.2021.0940
Subject(s) - trait , adaptation (eye) , biology , evolutionary biology , fitness landscape , ecology , computer science , population , demography , neuroscience , programming language , sociology
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 CO 2 . 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.