The effects of genetics, mutation and selection on Evolutionary Rescue in complex environments

过去150年的现代进化生物学使我们很好地理解了自然种群如何适应环境中不断变化的环境。然而，一个仍然知之甚少的重要案例是进化拯救（ER），即种群依靠适应来逃避由于大规模突然的环境变化而导致的衰退和灭绝。理解允许或限制ER的因素在基础和应用背景下都是根本和紧迫的。人类干预措施，如抗生素或农药治疗，旨在对目标物种施加无法克服的适应性挑战，了解ER的限制对于最大限度地提高这些干预措施的效力至关重要。相反，在管理受快速气候变化威胁的物种时，了解有利于ER的因素至关重要。鉴于其重要性，更好地了解是什么决定了种群的ER能力至关重要。先前的工作已经表明，ER的能力受到有利遗传变异的供应的限制，并且随着常设变异的量、新变异的输入（通过突变和移民）以及来自先前暴露于类似选择压力的预适应而增加。然而，一个重要的限制是，以前的工作ER主要集中在简单的环境和变化的意思是一个单一的环境变量。这是不现实的，因为生物体生活和进化在复杂的环境中，使用单变量环境无法捕捉多变量进化的复杂性，其中适应性反应可以受到显着影响（积极或消极）之间的遗传相关性对不同的变量。为了做到这一点，我们研究了一个群体显示ER的能力如何与不同的力量有关，这些力量塑造了对不同环境变量的遗传（共）变异，即环境响应背后的遗传途径的大小和拓扑结构，随机遗传突变，和过去的选择压力，形成突变变异的数量和方向。我们的项目使用高度可塑性的裂变酵母系统，并利用强大的表型和遗传高通量方法的组合。使用高度重复的生长测定在三个环境变量的梯度，氯化钾和氯化镁的浓度和温度，我们将确定这些变量的响应的基因和途径，以及它们重叠的程度。利用突变积累，我们将研究随机突变如何与这些途径的大小，结构和重叠相互作用，以产生环境响应的协变。此外，在不同的制度下的多变量环境波动的实验进化，我们将产生不同的选择历史的人口。最后，我们将评估这些进化种群中的ER，以及其他由选定的野生菌株组装的种群，当受到环境变量的均值，方差和协方差的变化时，在我们的项目过程中收集的数据将使我们能够更好地了解复杂环境中的ER，从而填补了我们对适应性进化的理解的一个主要空白。同时，我们将创建一个独特的完整的多变量进化案例研究，从基因型-表型图的遗传学基础到突变和选择，再到多变量性状空间的进化变化。重要的是，这些对决定ER的因素的见解也将立即在应用科学的几个领域中得到利用，包括濒危物种的管理以及药物和杀虫剂处理的设计（见影响）。

The past 150 years of modern evolutionary biology have provided us with a good understanding of how natural populations adapt to the constant changes that they experience in their environment. One important case that remains poorly understood, however, is that of evolutionary rescue (ER), where populations rely on adaptation to escape decline and extinction due to large, abrupt environmental shifts. Understanding the factors that allow or limit ER is fundamental and urgent in both basic and applied contexts. Human interventions such as antibiotic or pesticide treatments aim to impose unsurmountable adaptive challenges on the targeted species, and understanding the limits of ER is essential for maximising the efficacy of these interventions. Conversely, understanding the factors that favour ER is crucial when managing species threatened by rapid climate change.Given its importance, it is crucial to gain a better understanding of what determines a population's capacity for ER. Previous work has demonstrated that the capacity for ER is limited by the supply in advantageous genetic variants and increases with the amount of standing variation, the input of new variants (via mutation and immigration) and pre-adaptation from previous exposure to similar selection pressures. One significant limitation, however, is that previous work on ER has largely focussed on simple environments and changes in the mean of a single environmental variable. This is not realistic, because organisms live and evolve in complex environments, and using univariate environments fails to capture the complexity of multivariate evolution, where adaptive responses can be significantly affected (positively or negatively) by genetic correlations between responses to different variables.The aim of this project is to gain a detailed understanding of ER in multivariate environments. In order to do so, we investigate how the capacity of a population to show ER relates to the different forces that shape the genetic (co)variation for responses to different environmental variables, namely the size and topology of the genetic pathways underlying environmental responses, random genetic mutation, and past selective pressures that shape the amount and orientation of mutational variation.Our project uses the highly malleable fission yeast system and exploits a combination of powerful phenotypic and genetic high-throughput approaches. Using highly replicated growth assays across gradients of three environmental variables, the concentrations of potassium and magnesium chloride and temperature, we will determine the genes and pathways underlying responses to these variables and the degree to which they overlap. Using mutation accumulation, we will then study how random mutations interact with the size, structure and overlap of these pathways to generate covariation in environmental responses. Further, using experimental evolution under different regimes of multivariate environmental fluctuations, we will generate populations with different selective histories. Finally, we will assess ER in these evolved populations, alongside others assembled from selected wild strains, when subjected to changes in the means, variances and covariances of environmental variables.The data collected during the course of our project will allow us to generate a much better understanding of ER in complex environments, thus filling a major gap in our understanding of adaptive evolution. At the same time, we will have created a uniquely complete case study of multivariate evolution that ranges from the genetics underlying the genotype-phenotype map over mutation and selection to evolutionary change in multivariate trait space.Importantly, these insights into the factors determining ER will also be immediately exploitable in several areas of applied science, including the management of endangered species and the design of drug and pesticide treatments (see Impact).