PDF(Pubmed)
Abstract：
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.
摘要：
微生物构成食物网的基础并驱动生物地球化学循环。由于相互作用的环境和性状组合的数量众多，预测微生物进化对全球元素循环的影响仍然是一个重大挑战。这里,我们提出了一种将多变量特征数据整合到特征进化预测模型中的方法。我们调查了使用来自暴露于高CO2的藻类衣藻的经验演化数据参数化的数千个可能的适应性行走的结果。我们发现，历史偏见的方向（现有的性状相关性）影响了适应率和进化的表型（性状组合）。严重的,我们使用直接从经验性状值得出的适应度景观来捕获已知的进化现象。这项工作表明，生态模型需要同时表示性状的变化和性状之间相关性的变化，以便准确捕获浮游植物的进化并预测元素循环的未来变化。
