Evolving trait correlations underlie multi-trait adaptation 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 use empirical evolution data to first parameterize our framework followed by modeling thousands of possible adaptive walks. 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 elucidating low-fitness regions. The emergence of nonrandom phenotypic solutions and high-fitness areas in an empirical algal trait-scape demonstrates that a limited set of evolutionary trajectories underlie the vast amount of possible trait correlation scenarios. Investigating microbial evolution through a reduced set of biogeochemically-important 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. Author SummaryMicroorganisms drive the base of global food webs and biogeochemical cycling, which significantly regulates Earths climate. Thus, it is critical to understand how their evolutionary responses to global change will affect global environmental processes. Microbial populations are highly diverse and both shape and are shaped by numerous environmental gradients happening simultaneously on different timescales. The sheer number of combinations to experimentally test exceeds our ability to do so, and so theoretical approaches that integrate biological and environmental variability onto a reduced set of representative axes can aid predictions of evolutionary outcomes. Here, we show that only a limited number of evolutionary solutions underlie thousands of possible biological trait correlation combinations, and that depending on historical bias, some phenotypes are more probable than others. These phenotypes further converge into high-fitness regions in a collapsed trait landscape derived from these representative axes. The emergence of only a handful of solutions from thousands of possible scenarios is a powerful tool to help predict probable microbial evolutionary trajectories given a vast array of combinations. This approach lays the groundwork to embed this framework into larger ecosystem models to examine the effects of these responses on biogeochemical cycling and global climate.