Adaptations of Antarctic penguins in a rapidly changing environment

在人为气候变化的时代，了解物种对环境变化的反应是进化生物学的核心挑战。在快速的人为气候变化下，长期的遗传变异是否有助于持久力仍然是一个悬而未决的问题，转录灵活性等更快速的过程所起的作用也是一个悬而未决的问题。南大洋企鹅是气候变化的哨兵，也是研究适应的独特的野外系统，因为它们分布在地球上变化最快的区域之一的急剧环境梯度上。这个项目将在我们之前在理解企鹅生物多样性模式方面取得的进展的基础上，对企鹅适应环境的分子变异进行深入的研究。该项目的总体目标是通过评估企鹅目前如何适应不同的环境，帮助了解气候变化对南大洋企鹅的影响。这是一个广泛的项目，有许多可能的调查路线，并鼓励候选人根据自己的兴趣制定具体的目标。我们将使用分布在纬度梯度上的种群的基因组和功能基因组数据集来研究对不同环境的适应模式。将分析种群内和种群间的基因组、转录和表观基因组变异。定向选择下的基因将通过全基因组比较、基于窗口的FST估计和全基因组关联研究来确定，以检测多基因选择。将评估不同群体之间的差异基因表达，并进行基因本体论和共表达网络分析，以推断选择候选者的功能作用。将使用基于机器学习回归树的方法来确定分子变异的子集是否可以由环境来解释，以及哪些环境变量正在推动局部适应。

In the era of anthropogenic climate change, understanding species responses to environmental shifts is a central challenge in evolutionary biology. Whether standing genetic variation can aid persistence under rapid anthropogenic climate change remains an open question, as does the role of more rapid processes such as transcriptomic flexibility. Southern Ocean penguins are sentinels of climate change and a uniquely suited field system for studying adaptation, owing to their distribution across sharp environmental gradients in one of the most rapidly changing regions on Earth. This project will build on our previous advances in understanding patterns of penguin biodiversity to conduct an in-depth study of the landscape of environment-adapted molecular variation in penguins. The overall aim of this project is to contribute to understanding the impacts of climate change on Southern Ocean penguins by assessing how they are adapted to divergent environments now. This is a broad project with many possible lines of inquiry, and the candidate is encouraged to develop specific aims according to their own interests. We will use genomic and functional genomic datasets for populations distributed across latitudinal gradients to investigate patterns of adaptation to divergent environments. Genomic, transcriptomic and epigenomic variation both within and among populations will be analysed. Genes under directional selection will be identified using whole genome comparisons, via window-based estimates of FST and genome wide association studies to detect polygenic selection. Differential gene expression among populations will be assessed, and gene ontology and co-expression network analyses carried out to infer the functional roles of selection candidates. A machine-learning regression tree-based approach will be used to determine whether a subset of molecular variation can be explained by environment, and which environmental variables are driving local adaptation.