The evolution of trait correlations constrains phenotypic adaptation to high CO₂in a eukaryotic alga

Abstract

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 Earth’s 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.

The evolution of trait correlations constrains phenotypic adaptation to high CO2in a eukaryotic alga

“…We need to bridge the gap between evolutionary models and trait-based ecosystems models (ODE models) in order to better predict how marine microbes will adapt to shifts in the environment [ 11 , 46 ]. This work takes a critical first step in developing a framework (TRACE) which uses empirically derived multivariate trait-based landscapes to provide insight into the interaction between historical bias (trait correlations) and evolved phenotypes for marine phytoplankton.…”

“…Data accessibility. The model code is available at https://github.com/ LevineLab and a version of the manuscript is available from the biology preprint server bioRxiv: https://www.biorxiv.org/content/ 10.1101/2020.08.04.237230 [47].…”

Temperature variability interacts with mean temperature to influence the predictability of microbial phenotypes

“…Although many ocean biogeochemical models include correlations between traits (e.g., those that incorporate flexible C:N:P stoichiometry), many overlook how these correlations may change with adaptation to climate change. Collapsing multi‐trait phenotypes, derived from experiments such as this, into two dimensions using multi‐variate methods may provide a pathway for integrating plasticity and evolution into ocean biogeochemical models (Argyle et al, 2021; Walworth et al, 2021).…”

A High-Throughput Assay for Quantifying Phenotypic Traits of Microalgae

“…Trait-plasticity is rarely included in biogeochemical models, however it is highly relevant given the high phenotypic diversity known in the phytoplankton and more specifically in diatoms (Godhe and Rynearson, 2017). The QPA therefore provides a tool to estimate trait variation and relationships that could be used to inform trait-based models of microbial evolution (Walworth et al, 2021).…”

“…However, due to trade-offs between traits, there are potential limitations on the feasible trait combinations that can manifest both as plastic and evolutionary responses. These limitations on phenotypic expression may shape trajectories along which phytoplankton may evolve (Hinners et al, in prep) and can be used to inform more nuanced, trait-based models of phytoplankton evolution (Walworth et al, 2021).…”