Genomic approaches to inference of population history and multispecies community assembly

长期以来，物种之间以及物种内部种群之间的关系一直被比作树上的树枝--达尔文自己的笔记中就有一个众所周知的例子。自从DNA测序的发展以来，人们已经开发了一些方法，可以从序列中重建种群之间的历史关系。了解种群之间的历史关系(包括祖先种群分裂为子代种群，以及个体在种群之间的扩散)在生物学的许多领域都很重要，包括我们自己的物种在哪里和何时进化。在保护生物学中，种群历史的重建使我们能够确定哪里的物种可能具有最大的遗传多样性(这样我们就知道在哪里集中保护工作)，哪些种群通过迁徙联系在一起(因此可能相互支持)，哪些种群是孤立的(因此面临更高的灭绝风险)。更广泛地说，如果我们想要正确地理解种群对自然选择的反应，推断种群关系也是必不可少的。人口历史通常是使用在许多个体中采样的少量基因(通常是5个或更少)的数据来推断的。造成这种情况的一个主要原因是很难获得更多基因的序列数据，而且人们相信，为了了解正在发生的事情，有必要对许多个体进行采样。最近的重大进展改变了这种观点。首先，通过开发新的“NextGen”测序技术，序列数据的可用性已经有了巨大的飞跃，能够为任何物种的基因组中的数千个基因生成数据。其次，结合理论的进步表明，在少数个体中采样多个基因要比在少数个体中采样多个基因要好得多(这是常见的做法)。这是令人兴奋的，因为这意味着我们甚至可以研究不受欢迎或不可能对许多个体进行采样的稀有动物。然而，基因组数据集的巨大规模使得用现有方法分析它们变得困难或不可能。这个项目的一个主要目标是开发更好的工具来从基因组数据集推断种群历史，这些工具将在网络上提供给所有人使用。然后，我们将把我们的新工具应用于两个自然昆虫群落(欧洲橡树胆和东澳大利亚无花果)的真实数据，每个群落都由草食动物和它们的寄生蜂天敌组成。通过比较每个群落中不同物种的种群历史，我们将测试食草动物和寄生虫是否在空间和时间上一起传播，或者在一系列时间尺度上加入它们的群落。这是当前生态学研究的一个重要领域，因为物种之间的长期联系通常会导致强烈的生态依赖，这种相互作用的破坏(例如，通过人类强加的栖息地变化)可能很难恢复。我们还将测试一个更具体的假设，即食草动物可以暂时躲避它们的天敌，因此可以享受一定程度的“无敌时间”。我们之所以选择它们，部分是因为它们在多物种相互作用研究中作为模型系统的重要性，部分是因为所有涉及的昆虫物种遗传学的一个方面(雄性只有一组染色体的存在)使得为它们生成和分析基因组数据集特别容易。虽然这个例子关注的是与生物多样性相关的问题，但我们将开发的方法可以同样应用于更多应用的关联，例如人类与其寄生虫之间的关联。从少数个体中提取信息的能力为更好地利用现有样本或将对稀有物种的影响降至最低提供了巨大的潜力。这些机会将在通过该项目支持的3个研讨会上与利益攸关方讨论，并在支持的SSERC暑期学校向学校教师传达。

Relationships between species, and between populations within species, have long been likened to branches in a tree - Darwin's own notes include a well-known example. Since the development of DNA sequencing, methods have been developed that allow reconstruction of historical relationships among populations from sequence. Understanding of historical relationships among populations (which includes both the splitting of ancestral populations into daughter populations, and dispersal of individuals between populations) is important in many areas of biology, including where and when our own species evolved. In conservation biology, reconstruction of population history allows us to identify where species are likely to have their greatest genetic diversity (so we know where to concentrate conservation efforts), which populations are connected by migration (and so may support each other) and which are isolated (and so at higher risk of extinction). More generally, inferring population relationships is also essential if we want to correctly understand how populations are responding to natural selection.Population history is usually inferred using data for only a small number of genes (usually 5 or less), sampled in lots of individuals. A major reason for this has been the difficulty in getting sequence data for more genes, and a belief that sampling of many individuals is necessary to understand what is going on. Recent major advances have changed this view. First, there has been a quantum leap in availability of sequence data through development of new "nextgen" sequencing technologies, able to generate data for thousands of genes across the genome of any species. Second, advances in coalescent theory show that it is much better to sample many genes in a small number of individuals than vice versa (the common practice). This is exciting because it means we can work even with rare animals for which sampling of many individuals is unwelcome or impossible. However, the sheer size of genomic datasets makes it difficult or impossible to analyse them with available methods. A major aim of this project is the development of better tools for inference of population history from genomic datasets, which will be made available on the web for all to use.We will then apply our new tools to real data for two natural insect communities (European oak galls and eastern Australian figs), each of which comprises herbivores and their parasitoid wasp natural enemies. By comparing population histories across species in each community, we will test whether herbivores and parasitoids spread together through space and time, or joined their communities over a range of timescales. This is a major area of current research in ecology that matters because long associations between species commonly result in strong ecological dependence, and disruptions of such interactions (for example through human-imposed habitat change) can be very hard to restore. We will also test the more specific hypothesis that herbivores can escape their natural enemies for a while, and so enjoy a measure of 'enemy-free time'. We choose these in part because of their importance as model systems in the study of multispecies interactions, and in part because an aspect of the genetics of all the insect species involved (presence of a single set of chromosomes in males) makes it particularly easy to generate and analyse genomic datasets for them. And while this example focuses on a biodiversity-related issue, the methods we will develop can be applied equally to more applied associations, such as those between humankind and their parasites.Ability to extract information from small numbers of individuals provides enormous potential to make better use of existing samples, or minimise impact on rare species. These opportunities will be discussed with stakeholders at 3 supported workshops through the project, and communicated to school teachers in a supported SSERC summer school.