Multivariate trait correlational evolution of ecologically important traits underlies constrained phenotypic adaptation to high CO2 in a eukaryotic alga

Microbes form the base of food webs and drive both aquatic and terrestrial biogeochemical cycling, thereby significantly influencing the global climate. Predicting how microbes will adapt to global change and the implications for global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here we present an approach for modeling multivariate trait evolution using orthogonal axes to define a trait-scape. We investigate the outcome of thousands of possible adaptive walks within a trait-scape parameterized using empirical evolution data. We find that only a limited number of phenotypes emerge, with some being more probable than others. Populations with historical bias in the direction of selection exhibited accelerated adaptation while highly convergent phenotypes emerged irrespective of the type of bias. Reproducible phenotypes further converged into several high-fitness regions in the collapsed trait-scape, thereby defining probable low-fitness (exclusion) regions. The emergence of nonrandom phenotypic solutions and high-fitness areas in an empirical algal trait-scape confirms that a limited set of evolutionary trajectories underlie the vast amount of possible trait correlation scenarios. Critically, we demonstrate these dynamics in multidimensional trait space and show that trait correlations, in addition to trait values, must evolve to explain the trajectories and outcomes of adaptation in multi-trait space. Investigating microbial evolution through a reduced set of evolvable biogeochemically-important traits and trait relationships lays the groundwork for incorporation into global change-driven ecosystem models where microbial trait dispersal can occur through different inheritance mechanisms. Identifying the probabilities of high-fitness outcomes based on trait correlations will be critical to directly connect microbial evolutionary responses to biogeochemical cycling under dynamic global change scenarios.